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Yang Y, Zhu F, Zhang X, Chen P, Wang Y, Zhu J, Ding Y, Cheng L, Li C, Jiang H, Wang Z, Lin P, Shi T, Wang M, Liu Q, Xu N, Liu M. Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance. Nat Commun 2024; 15:4318. [PMID: 38773067 PMCID: PMC11109161 DOI: 10.1038/s41467-024-48399-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 04/30/2024] [Indexed: 05/23/2024] Open
Abstract
Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.
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Affiliation(s)
- Yue Yang
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China
| | - Fangduo Zhu
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Xumeng Zhang
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China.
| | - Pei Chen
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Yongzhou Wang
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China
| | - Jiaxue Zhu
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China
| | - Yanting Ding
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Lingli Cheng
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China
| | - Chao Li
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China
| | - Hao Jiang
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Zhongrui Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077, China
| | - Peng Lin
- College of Computer Science and Technology, Zhejiang University, Zhejiang, 310027, China
| | - Tuo Shi
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China
| | - Ming Wang
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Qi Liu
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China.
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China.
| | - Ningsheng Xu
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Ming Liu
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China
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2
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Li K, Yao J, Zhao P, Luo Y, Ge X, Yang R, Cheng X, Miao X. Ovonic threshold switching-based artificial afferent neurons for thermal in-sensor computing. MATERIALS HORIZONS 2024; 11:2106-2114. [PMID: 38545857 DOI: 10.1039/d4mh00053f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Artificial afferent neurons in the sensory nervous system inspired by biology have enormous potential for efficiently perceiving and processing environmental information. However, the previously reported artificial afferent neurons suffer from two prominent challenges: considerable power consumption and limited scalability efficiency. Herein, addressing these challenges, a bioinspired artificial thermal afferent neuron based on a N-doped SiTe ovonic threshold switching (OTS) device is presented for the first time. The engineered OTS device shows remarkable uniformity and robust endurance, ensuring the reliability and efficacy of the artificial afferent neurons. A substantially decreased leakage current of the SiTe OTS device by nitrogen doping results in ultra-low power consumption less than 0.3 nJ per spike for artificial afferent neurons. The inherent temperature response exhibited by N-doped SiTe OTS materials allows us to construct a highly compact artificial thermal afferent neuron over a wide temperature range. An edge detection task is performed to further verify its thermal perceptual computing function. Our work provides an insight into OTS-based artificial afferent neurons for electronic skin and sensory neurorobotics.
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Affiliation(s)
- Kai Li
- School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Jiaping Yao
- School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Peng Zhao
- School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Yunhao Luo
- School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Xiang Ge
- School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Rui Yang
- School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Xiaomin Cheng
- School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Xiangshui Miao
- School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
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3
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Bruno U, Rana D, Ausilio C, Mariano A, Bettucci O, Musall S, Lubrano C, Santoro F. An organic brain-inspired platform with neurotransmitter closed-loop control, actuation and reinforcement learning. MATERIALS HORIZONS 2024. [PMID: 38698769 DOI: 10.1039/d3mh02202a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Organic neuromorphic platforms have recently received growing interest for the implementation and integration of artificial and hybrid neuronal networks. Here, achieving closed-loop and learning/training processes as in the human brain is still a major challenge especially exploiting time-dependent biosignalling such as neurotransmitter release. Here, we present an integrated organic platform capable of cooperating with standard silicon technologies, to achieve brain-inspired computing via adaptive synaptic potentiation and depression, in a closed-loop fashion. The microfabricated platform could be interfaced and control a robotic hand which ultimately was able to learn the grasping of differently sized objects, autonomously.
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Affiliation(s)
- Ugo Bruno
- Tissue Electronics, Istituto Italiano di Tecnologia, 80125, Naples, Italy
- Dipartimento di Chimica, Materiali e Produzione Industriale, Università di Napoli Federico II, 80125, Naples, Italy
| | - Daniela Rana
- Institute of Biological Information Processing - Bioelectronics, IBI-3, Forschungszentrum Juelich, 52428, Germany
- Neuroelectronic Interfaces, Faculty of Electrical Engineering and IT, RWTH Aachen, 52074, Germany
| | - Chiara Ausilio
- Tissue Electronics, Istituto Italiano di Tecnologia, 80125, Naples, Italy
- Dipartimento di Chimica, Materiali e Produzione Industriale, Università di Napoli Federico II, 80125, Naples, Italy
| | - Anna Mariano
- Tissue Electronics, Istituto Italiano di Tecnologia, 80125, Naples, Italy
| | - Ottavia Bettucci
- Tissue Electronics, Istituto Italiano di Tecnologia, 80125, Naples, Italy
| | - Simon Musall
- Institute of Biological Information Processing - Bioelectronics, IBI-3, Forschungszentrum Juelich, 52428, Germany
- Faculty of Medicine, Institute of Experimental Epileptology and Cognition Research, University of Bonn, Bonn, Germany
| | - Claudia Lubrano
- Institute of Biological Information Processing - Bioelectronics, IBI-3, Forschungszentrum Juelich, 52428, Germany
- Neuroelectronic Interfaces, Faculty of Electrical Engineering and IT, RWTH Aachen, 52074, Germany
| | - Francesca Santoro
- Tissue Electronics, Istituto Italiano di Tecnologia, 80125, Naples, Italy
- Institute of Biological Information Processing - Bioelectronics, IBI-3, Forschungszentrum Juelich, 52428, Germany
- Neuroelectronic Interfaces, Faculty of Electrical Engineering and IT, RWTH Aachen, 52074, Germany
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4
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Li R, Rezaeiyan Y, Böhnert T, Schulman A, Ferreira R, Farkhani H, Moradi F. Temperature effect on a weighted vortex spin-torque nano-oscillator for neuromorphic computing. Sci Rep 2024; 14:10043. [PMID: 38698145 PMCID: PMC11065860 DOI: 10.1038/s41598-024-60929-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 04/26/2024] [Indexed: 05/05/2024] Open
Abstract
In this work, we present fabricated magnetic tunnel junctions (MTJs) that can serve as magnetic memories (MMs) or vortex spin-torque nano-oscillators (STNOs) depending on the device geometry. We explore the heating effect on the devices to study how the performance of a neuromorphic computing system (NCS) consisting of MMs and STNOs can be enhanced by temperature. We further applied a neural network for waveform classification applications. The resistance of MMs represents the synaptic weights of the NCS, while temperature acts as an extra degree of freedom in changing the weights and TMR, as their anti-parallel resistance is temperature sensitive, and parallel resistance is temperature independent. Given the advantage of using heat for such a network, we envision using a vertical-cavity surface-emitting laser (VCSEL) to selectively heat MMs and/or STNO when needed. We found that when heating MMs only, STNO only, or both MMs and STNO, from 25 to 75 °C, the output power of the STNO increases by 24.7%, 72%, and 92.3%, respectively. Our study shows that temperature can be used to improve the output power of neural networks, and we intend to pave the way for future implementation of a low-area and high-speed VCSEL-assisted spintronic NCS.
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Affiliation(s)
- Ren Li
- Department of Electrical and Computer Engineering, Aarhus University, 8200, Aarhus, Denmark.
| | - Yasser Rezaeiyan
- Department of Electrical and Computer Engineering, Aarhus University, 8200, Aarhus, Denmark
| | - Tim Böhnert
- INL-International Iberian Nanotechnology Laboratory, 4715-330, Braga, Portugal
| | - Alejandro Schulman
- INL-International Iberian Nanotechnology Laboratory, 4715-330, Braga, Portugal
| | - Ricardo Ferreira
- INL-International Iberian Nanotechnology Laboratory, 4715-330, Braga, Portugal
| | - Hooman Farkhani
- Department of Electrical and Computer Engineering, Aarhus University, 8200, Aarhus, Denmark
| | - Farshad Moradi
- Department of Electrical and Computer Engineering, Aarhus University, 8200, Aarhus, Denmark.
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5
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Nibhanupudi SST, Roy A, Chowdhury S, Schalip R, Coupin MJ, Matthews KC, Alam MH, Satpati B, Movva HCP, Luth CJ, Wu S, Warner JH, Banerjee SK. Low-Temperature Synthesis of WSe 2 by the Selenization Process under Ultrahigh Vacuum for BEOL Compatible Reconfigurable Neurons. ACS APPLIED MATERIALS & INTERFACES 2024; 16:22326-22333. [PMID: 38635965 DOI: 10.1021/acsami.3c18446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
Low-temperature large-area growth of two-dimensional (2D) transition-metal dichalcogenides (TMDs) is critical for their integration with silicon chips. Especially, if the growth temperatures can be lowered below the back-end-of-line (BEOL) processing temperatures, the Si transistors can interface with 2D devices (in the back end) to enable high-density heterogeneous circuits. Such configurations are particularly useful for neuromorphic computing applications where a dense network of neurons interacts to compute the output. In this work, we present low-temperature synthesis (400 °C) of 2D tungsten diselenide (WSe2) via the selenization of the W film under ultrahigh vacuum (UHV) conditions. This simple yet effective process yields large-area, homogeneous films of 2D TMDs, as confirmed by several characterization techniques, including reflection high-energy electron diffraction, atomic force microscopy, transmission electron microscopy, and different spectroscopy methods. Memristors fabricated using the grown WSe2 film are leveraged to realize a novel compact neuron circuit that can be reconfigured to enable homeostasis.
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Affiliation(s)
- S S Teja Nibhanupudi
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Anupam Roy
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
- Department of Physics, Birla Institute of Technology Mesra, Ranchi, Jharkhand 835215, India
| | - Sayema Chowdhury
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Ryan Schalip
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Matthew J Coupin
- Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Kevin C Matthews
- Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Md Hasibul Alam
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Biswarup Satpati
- Surface Physics and Material Science Division, Saha Institute of Nuclear Physics, 1/AF, Bidhannagar, Kolkata 700 064, India
| | - Hema C P Movva
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Christopher J Luth
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Siyu Wu
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Jamie H Warner
- Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Sanjay K Banerjee
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
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6
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Gehrig D, Scaramuzza D. Low-latency automotive vision with event cameras. Nature 2024; 629:1034-1040. [PMID: 38811712 PMCID: PMC11136662 DOI: 10.1038/s41586-024-07409-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 04/10/2024] [Indexed: 05/31/2024]
Abstract
The computer vision algorithms used currently in advanced driver assistance systems rely on image-based RGB cameras, leading to a critical bandwidth-latency trade-off for delivering safe driving experiences. To address this, event cameras have emerged as alternative vision sensors. Event cameras measure the changes in intensity asynchronously, offering high temporal resolution and sparsity, markedly reducing bandwidth and latency requirements1. Despite these advantages, event-camera-based algorithms are either highly efficient but lag behind image-based ones in terms of accuracy or sacrifice the sparsity and efficiency of events to achieve comparable results. To overcome this, here we propose a hybrid event- and frame-based object detector that preserves the advantages of each modality and thus does not suffer from this trade-off. Our method exploits the high temporal resolution and sparsity of events and the rich but low temporal resolution information in standard images to generate efficient, high-rate object detections, reducing perceptual and computational latency. We show that the use of a 20 frames per second (fps) RGB camera plus an event camera can achieve the same latency as a 5,000-fps camera with the bandwidth of a 45-fps camera without compromising accuracy. Our approach paves the way for efficient and robust perception in edge-case scenarios by uncovering the potential of event cameras2.
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Affiliation(s)
- Daniel Gehrig
- Robotics and Perception Group, University of Zurich, Zurich, Switzerland.
| | - Davide Scaramuzza
- Robotics and Perception Group, University of Zurich, Zurich, Switzerland.
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7
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El Srouji L, Abdelghany M, Ambethkar HR, Lee YJ, Berkay On M, Yoo SJB. Perspective: an optoelectronic future for heterogeneous, dendritic computing. Front Neurosci 2024; 18:1394271. [PMID: 38699677 PMCID: PMC11064649 DOI: 10.3389/fnins.2024.1394271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/08/2024] [Indexed: 05/05/2024] Open
Abstract
With the increasing number of applications reliant on large neural network models, the pursuit of more suitable computing architectures is becoming increasingly relevant. Progress toward co-integrated silicon photonic and CMOS circuits provides new opportunities for computing architectures with high bandwidth optical networks and high-speed computing. In this paper, we discuss trends in neuromorphic computing architecture and outline an optoelectronic future for heterogeneous, dendritic neuromorphic computing.
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Affiliation(s)
| | | | | | | | | | - S. J. Ben Yoo
- Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA, United States
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8
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Kim Y, Kim H, Oh K, Park JH, Baek CK. Highly biomimetic spiking neuron using SiGe heterojunction bipolar transistors for energy-efficient neuromorphic systems. Sci Rep 2024; 14:8356. [PMID: 38594291 PMCID: PMC11004001 DOI: 10.1038/s41598-024-58962-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 04/05/2024] [Indexed: 04/11/2024] Open
Abstract
We demonstrate a highly biomimetic spiking neuron capable of fast and energy-efficient neuronal oscillation dynamics. Our simple neuron circuit is constructed using silicon-germanium heterojunction based bipolar transistors (HBTs) with nanowire structure. The HBT has a hysteresis window with steep switching characteristics and high current margin in the low voltage range, which enables a high spiking frequency (~ 245 kHz) with low energy consumption (≤ 1.37 pJ/spike). Also, gated structure achieves a stable balance in the activity of the neural system by incorporating both excitatory and inhibitory signal. Furthermore, inhibition of multiple strengths can be realized by adjusting the integration time according to the amplitude of the inhibitory signal. In addition, the spiking frequency can be tuned by mutually controlling the hysteresis window in the HBTs. These results ensure the sparse activity and homeostasis of neural networks.
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Affiliation(s)
- Yijoon Kim
- Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea
| | - Hyangwoo Kim
- Future IT Innovation Laboratory, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea
| | - Kyounghwan Oh
- Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea
| | - Ju Hong Park
- Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea
| | - Chang-Ki Baek
- Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea.
- Future IT Innovation Laboratory, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea.
- Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea.
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9
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Philip P, Jainwal K, van Schaik A, Thakur CS. Tau-Cell-Based Analog Silicon Retina With Spatio- Temporal Filtering and Contrast Gain Control. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2024; 18:423-437. [PMID: 37956014 DOI: 10.1109/tbcas.2023.3332117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Developing precise artificial retinas is crucial because they hold the potential to restore vision, improve visual prosthetics, and enhance computer vision systems. Emulating the luminance and contrast adaption features of the retina is essential to improve visual perception and efficiency to provide an environment realistic representation to the user. In this article, we introduce an artificial retina model that leverages its potent adaptation to luminance and contrast to enhance vision sensing and information processing. The model has the ability to achieve the realization of both tonic and phasic cells in the simplest manner. We have implemented the retina model using 0.18 μm process technology and validated the accuracy of the hardware implementation through circuit simulation that closely matches the software retina model. Additionally, we have characterized a single pixel fabricated using the same 0.18 μm process. This pixel demonstrates an 87.7-% ratio of variance with the temporal software model and operates with a power consumption of 369 nW.
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10
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Ge Y, Liu R, Zhang G, Daoud MS, Zhang Q, Huang X, Mayet AM, Chen ZM, He S. High-Matching and Low-Cost Realization of the FHN Neuron Model on Reconfigurable FPGA Board. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2024; 18:451-459. [PMID: 38019637 DOI: 10.1109/tbcas.2023.3337335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
The main objectives of neuromorphic engineering are the research, modeling, and implementation of neural functioning in the human brain. We provide a hardware solution that can replicate such a nature-inspired system by merging multiple scientific domains and is based on neural cell processes. This work provides a modified version of the original Fitz-Hugh Nagumo (FHN) neuron using a simple 2V term called Hybrid Piece-Wised Base-2 Model (HPWBM), which accurately reproduces numerous patterns of the original neuron model. With reduced terms, we suggest modifying the original nonlinear term to achieve high matching accuracy and little computing error. Time domain and phase portraits are used to validate the proposed model, which shows that it can reproduce all of the FHN model's properties with high accuracy and little mistake. We provide an effective digital hardware approach for large-scale neuron implementations based on resource-sharing and pipelining strategies. The Hardware Description Language (HDL) is used to construct the hardware on an FPGA as a proof of concept. The recommended model hardly uses 0.48 percent of the resources on a Virtex 4 FPGA board, according to the results of the hardware implementation. The circuit can run at a maximum frequency of 448.236 MHz, according to the static timing study.
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11
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Wan C, Pei M, Shi K, Cui H, Long H, Qiao L, Xing Q, Wan Q. Toward a Brain-Neuromorphics Interface. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2311288. [PMID: 38339866 DOI: 10.1002/adma.202311288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/17/2024] [Indexed: 02/12/2024]
Abstract
Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.
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Affiliation(s)
- Changjin Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Mengjiao Pei
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Kailu Shi
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Hangyuan Cui
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Haotian Long
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Lesheng Qiao
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qianye Xing
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qing Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
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12
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Lee H, Cho J, Jin M, Lee JH, Lee C, Kim J, Lee J, Shin JC, Yoo J, Lee E, Kim YS. Electrochemical Analysis of Ion Effects on Electrolyte-Gated Synaptic Transistor Characteristics. ACS NANO 2024. [PMID: 38324887 DOI: 10.1021/acsnano.3c10082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Electrolyte-gated transistors (EGTs) are promising candidates as artificial synapses owing to their precise conductance controllability, quick response times, and especially their low operating voltages resulting from ion-assisted signal transmission. However, it is still vague how ion-related physiochemical elements and working mechanisms impact synaptic performance. Here, to address the unclear correlations, we suggest a methodical approach based on electrochemical analysis using poly(ethylene oxide) EGTs with three alkali ions: Li+, Na+, and K+. Cyclic voltammetry is employed to identify the kind of electrochemical reactions taking place at the channel/electrolyte interface, which determines the nonvolatile memory functionality of the EGTs. Additionally, using electrochemical impedance spectroscopy and qualitative analysis of electrolytes, we confirm that the intrinsic properties of electrolytes (such as crystallinity, solubility, and ion conductivity) and ion dynamics ultimately define the linearity/symmetricity of conductance modulation. Through simple but systematic electrochemical analysis, these results offer useful insights for the selection of components for high-performing artificial synapses.
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Affiliation(s)
- Haeyeon Lee
- Department of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Jinil Cho
- Program in Nano Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Minho Jin
- Program in Nano Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Jae Hak Lee
- Program in Nano Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea
- Samsung Display Company, Ltd., 1 Samsung-ro, Giheung-gu, Yongin-si, Gyeonggi-do 17113, Republic of Korea
| | - Chan Lee
- Department of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Jiyeon Kim
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Jiho Lee
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Jong Chan Shin
- Department of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Jeeyoung Yoo
- School of Energy Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Eungkyu Lee
- Department of Electronic Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, Republic of Korea
| | - Youn Sang Kim
- Department of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea
- Program in Nano Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Republic of Korea
- Advanced Institutes of Convergence Technology, 145 Gwanggyo-ro, Yeongtong-gu, Suwon 16229, Republic of Korea
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13
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Qiu E, Zhang YH, Ventra MD, Schuller IK. Reconfigurable Cascaded Thermal Neuristors for Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2306818. [PMID: 37770043 DOI: 10.1002/adma.202306818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/25/2023] [Indexed: 10/03/2023]
Abstract
While the complementary metal-oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, an alternative route is explored based on a new class of spiking oscillators called "thermal neuristors", which operate and interact solely via thermal processes. Utilizing the insulator-to-metal transition (IMT) in vanadium dioxide, a wide variety of reconfigurable electrical dynamics mirroring biological neurons is demonstrated. Notably, inhibitory functionality is achieved just in a single oxide device, and cascaded information flow is realized exclusively through thermal interactions. To elucidate the underlying mechanisms of the neuristors, a detailed theoretical model is developed, which accurately reflects the experimental results. This study establishes the foundation for scalable and energy-efficient thermal neural networks, fostering progress in brain-inspired computing.
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Affiliation(s)
- Erbin Qiu
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Yuan-Hang Zhang
- Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA
| | | | - Ivan K Schuller
- Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA
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14
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Yang SJ, Liang L, Lee Y, Gu Y, Fatheema J, Kutagulla S, Kim D, Kim M, Kim S, Akinwande D. Volatile and Nonvolatile Resistive Switching Coexistence in Conductive Point Hexagonal Boron Nitride Monolayer. ACS NANO 2024; 18:3313-3322. [PMID: 38226861 DOI: 10.1021/acsnano.3c10068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Recently, we demonstrated the nonvolatile resistive switching effects of metal-insulator-metal (MIM) atomristor structures based on two-dimensional (2D) monolayers. However, there are many remaining combinations between 2D monolayers and metal electrodes; hence, there is a need to further explore 2D resistance switching devices from material selections to future perspectives. This study investigated the volatile and nonvolatile switching coexistence of monolayer hexagonal boron nitride (h-BN) atomristors using top and bottom silver (Ag) metal electrodes. Utilizing an h-BN monolayer and Ag electrodes, we found that the transition between volatile and nonvolatile switching is attributed to the thickness/stiffness of chain-like conductive bridges between h-BN and Ag surfaces based on the current compliance and atomristor area. Computations indicate a "weak" bridge is responsible for volatile switching, while a "strong" bridge is formed for nonvolatile switching. The current compliance determines the number of Ag atoms that undergo dissociation at the electrode, while the atomristor area determines the degree of electric field localization that forms more stable conductive bridges. The findings of this study suggest that the h-BN atomristor using Ag electrodes shows promise as a potential solution to integrate both volatile neurons and nonvolatile synapses in a single neuromorphic crossbar array structure through electrical and dimensional designs.
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Affiliation(s)
- Sung Jin Yang
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Liangbo Liang
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Yoonseok Lee
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Yuqian Gu
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Jameela Fatheema
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Shanmukh Kutagulla
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Dahyeon Kim
- Department of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, South Korea
| | - Myungsoo Kim
- Department of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, South Korea
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Deji Akinwande
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
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15
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Gemo E, Spiga S, Brivio S. SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks. Front Neurosci 2024; 17:1270090. [PMID: 38264497 PMCID: PMC10804805 DOI: 10.3389/fnins.2023.1270090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 12/14/2023] [Indexed: 01/25/2024] Open
Abstract
Investigations in the field of spiking neural networks (SNNs) encompass diverse, yet overlapping, scientific disciplines. Examples range from purely neuroscientific investigations, researches on computational aspects of neuroscience, or applicative-oriented studies aiming to improve SNNs performance or to develop artificial hardware counterparts. However, the simulation of SNNs is a complex task that can not be adequately addressed with a single platform applicable to all scenarios. The optimization of a simulation environment to meet specific metrics often entails compromises in other aspects. This computational challenge has led to an apparent dichotomy of approaches, with model-driven algorithms dedicated to the detailed simulation of biological networks, and data-driven algorithms designed for efficient processing of large input datasets. Nevertheless, material scientists, device physicists, and neuromorphic engineers who develop new technologies for spiking neuromorphic hardware solutions would find benefit in a simulation environment that borrows aspects from both approaches, thus facilitating modeling, analysis, and training of prospective SNN systems. This manuscript explores the numerical challenges deriving from the simulation of spiking neural networks, and introduces SHIP, Spiking (neural network) Hardware In PyTorch, a numerical tool that supports the investigation and/or validation of materials, devices, small circuit blocks within SNN architectures. SHIP facilitates the algorithmic definition of the models for the components of a network, the monitoring of states and output of the modeled systems, and the training of the synaptic weights of the network, by way of user-defined unsupervised learning rules or supervised training techniques derived from conventional machine learning. SHIP offers a valuable tool for researchers and developers in the field of hardware-based spiking neural networks, enabling efficient simulation and validation of novel technologies.
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Affiliation(s)
- Emanuele Gemo
- CNR–IMM, Unit of Agrate Brianza, Agrate Brianza, Italy
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16
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Taeckens EA, Shah S. A spiking neural network with continuous local learning for robust online brain machine interface. J Neural Eng 2024; 20:066042. [PMID: 38173230 DOI: 10.1088/1741-2552/ad1787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 12/20/2023] [Indexed: 01/05/2024]
Abstract
Objective.Spiking neural networks (SNNs) are powerful tools that are well suited for brain machine interfaces (BMI) due to their similarity to biological neural systems and computational efficiency. They have shown comparable accuracy to state-of-the-art methods, but current training methods require large amounts of memory, and they cannot be trained on a continuous input stream without pausing periodically to perform backpropagation. An ideal BMI should be capable training continuously without interruption to minimize disruption to the user and adapt to changing neural environments.Approach.We propose a continuous SNN weight update algorithm that can be trained to perform regression learning with no need for storing past spiking events in memory. As a result, the amount of memory needed for training is constant regardless of the input duration. We evaluate the accuracy of the network on recordings of neural data taken from the premotor cortex of a primate performing reaching tasks. Additionally, we evaluate the SNN in a simulated closed loop environment and observe its ability to adapt to sudden changes in the input neural structure.Main results.The continuous learning SNN achieves the same peak correlation (ρ=0.7) as existing SNN training methods when trained offline on real neural data while reducing the total memory usage by 92%. Additionally, it matches state-of-the-art accuracy in a closed loop environment, demonstrates adaptability when subjected to multiple types of neural input disruptions, and is capable of being trained online without any prior offline training.Significance.This work presents a neural decoding algorithm that can be trained rapidly in a closed loop setting. The algorithm increases the speed of acclimating a new user to the system and also can adapt to sudden changes in neural behavior with minimal disruption to the user.
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Affiliation(s)
- Elijah A Taeckens
- Department of Electrical and Computer Engineering, University of Maryland, College Park, United States of America
| | - Sahil Shah
- Department of Electrical and Computer Engineering, University of Maryland, College Park, United States of America
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17
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Dalgaty T, Moro F, Demirağ Y, De Pra A, Indiveri G, Vianello E, Payvand M. Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems. Nat Commun 2024; 15:142. [PMID: 38167293 PMCID: PMC10761708 DOI: 10.1038/s41467-023-44365-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
The brain's connectivity is locally dense and globally sparse, forming a small-world graph-a principle prevalent in the evolution of various species, suggesting a universal solution for efficient information routing. However, current artificial neural network circuit architectures do not fully embrace small-world neural network models. Here, we present the neuromorphic Mosaic: a non-von Neumann systolic architecture employing distributed memristors for in-memory computing and in-memory routing, efficiently implementing small-world graph topologies for Spiking Neural Networks (SNNs). We've designed, fabricated, and experimentally demonstrated the Mosaic's building blocks, using integrated memristors with 130 nm CMOS technology. We show that thanks to enforcing locality in the connectivity, routing efficiency of Mosaic is at least one order of magnitude higher than other SNN hardware platforms. This is while Mosaic achieves a competitive accuracy in a variety of edge benchmarks. Mosaic offers a scalable approach for edge systems based on distributed spike-based computing and in-memory routing.
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Affiliation(s)
| | - Filippo Moro
- CEA, LETI, Université Grenoble Alpes, Grenoble, France
| | - Yiğit Demirağ
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | | | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | | | - Melika Payvand
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
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18
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Stiefel KM, Coggan JS. The energy challenges of artificial superintelligence. Front Artif Intell 2023; 6:1240653. [PMID: 37941679 PMCID: PMC10629395 DOI: 10.3389/frai.2023.1240653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 10/05/2023] [Indexed: 11/10/2023] Open
Abstract
We argue here that contemporary semiconductor computing technology poses a significant if not insurmountable barrier to the emergence of any artificial general intelligence system, let alone one anticipated by many to be "superintelligent". This limit on artificial superintelligence (ASI) emerges from the energy requirements of a system that would be more intelligent but orders of magnitude less efficient in energy use than human brains. An ASI would have to supersede not only a single brain but a large population given the effects of collective behavior on the advancement of societies, further multiplying the energy requirement. A hypothetical ASI would likely consume orders of magnitude more energy than what is available in highly-industrialized nations. We estimate the energy use of ASI with an equation we term the "Erasi equation", for the Energy Requirement for Artificial SuperIntelligence. Additional efficiency consequences will emerge from the current unfocussed and scattered developmental trajectory of AI research. Taken together, these arguments suggest that the emergence of an ASI is highly unlikely in the foreseeable future based on current computer architectures, primarily due to energy constraints, with biomimicry or other new technologies being possible solutions.
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Affiliation(s)
| | - Jay S. Coggan
- NeuroLinx Research Institute, La Jolla, CA, United States
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19
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Han J, Lee J, Kim Y, Kim YB, Yun S, Lee S, Yu J, Lee KJ, Myung H, Choi Y. 3D Neuromorphic Hardware with Single Thin-Film Transistor Synapses Over Single Thin-Body Transistor Neurons by Monolithic Vertical Integration. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302380. [PMID: 37712147 PMCID: PMC10602577 DOI: 10.1002/advs.202302380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/24/2023] [Indexed: 09/16/2023]
Abstract
Neuromorphic hardware with a spiking neural network (SNN) can significantly enhance the energy efficiency for artificial intelligence (AI) functions owing to its event-driven and spatiotemporally sparse operations. However, an artificial neuron and synapse based on complex complementary metal-oxide-semiconductor (CMOS) circuits limit the scalability and energy efficiency of neuromorphic hardware. In this work, a neuromorphic module is demonstrated composed of synapses over neurons realized by monolithic vertical integration. The synapse at top is a single thin-film transistor (1TFT-synapse) made of poly-crystalline silicon film and the neuron at bottom is another single transistor (1T-neuron) made of single-crystalline silicon. Excimer laser annealing (ELA) is applied to activate dopants for the 1TFT-synapse at the top and rapid thermal annealing (RTA) is applied to do so for the 1T-neuron at the bottom. Internal electro-thermal annealing (ETA) via the generation of Joule heat is also used to enhance the endurance of the 1TFT-synapse without transferring heat to the 1T-neuron at the bottom. As neuromorphic vision sensing, classification of American Sign Language (ASL) is conducted with the fabricated neuromorphic module. Its classification accuracy on ASL is ≈92.3% even after 204 800 update pulses.
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Affiliation(s)
- Joon‐Kyu Han
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Jung‐Woo Lee
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
- SK Hynix Inc.Icheon17336Republic of Korea
| | - Yeeun Kim
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Young Bin Kim
- Department of Materials Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Seong‐Yun Yun
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Sang‐Won Lee
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Ji‐Man Yu
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Keon Jae Lee
- Department of Materials Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Hyun Myung
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Yang‐Kyu Choi
- School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
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20
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Pham MD, D’Angiulli A, Dehnavi MM, Chhabra R. From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems? Brain Sci 2023; 13:1316. [PMID: 37759917 PMCID: PMC10526461 DOI: 10.3390/brainsci13091316] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 09/05/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
We examine the challenging "marriage" between computational efficiency and biological plausibility-A crucial node in the domain of spiking neural networks at the intersection of neuroscience, artificial intelligence, and robotics. Through a transdisciplinary review, we retrace the historical and most recent constraining influences that these parallel fields have exerted on descriptive analysis of the brain, construction of predictive brain models, and ultimately, the embodiment of neural networks in an enacted robotic agent. We study models of Spiking Neural Networks (SNN) as the central means enabling autonomous and intelligent behaviors in biological systems. We then provide a critical comparison of the available hardware and software to emulate SNNs for investigating biological entities and their application on artificial systems. Neuromorphics is identified as a promising tool to embody SNNs in real physical systems and different neuromorphic chips are compared. The concepts required for describing SNNs are dissected and contextualized in the new no man's land between cognitive neuroscience and artificial intelligence. Although there are recent reviews on the application of neuromorphic computing in various modules of the guidance, navigation, and control of robotic systems, the focus of this paper is more on closing the cognition loop in SNN-embodied robotics. We argue that biologically viable spiking neuronal models used for electroencephalogram signals are excellent candidates for furthering our knowledge of the explainability of SNNs. We complete our survey by reviewing different robotic modules that can benefit from neuromorphic hardware, e.g., perception (with a focus on vision), localization, and cognition. We conclude that the tradeoff between symbolic computational power and biological plausibility of hardware can be best addressed by neuromorphics, whose presence in neurorobotics provides an accountable empirical testbench for investigating synthetic and natural embodied cognition. We argue this is where both theoretical and empirical future work should converge in multidisciplinary efforts involving neuroscience, artificial intelligence, and robotics.
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Affiliation(s)
- Martin Do Pham
- Department of Computer Science, University of Toronto, Toronto, ON M5S 1A1, Canada; (M.D.P.); (M.M.D.)
| | - Amedeo D’Angiulli
- Department of Neuroscience, Carleton University, Ottawa, ON K1S 5B6, Canada;
| | - Maryam Mehri Dehnavi
- Department of Computer Science, University of Toronto, Toronto, ON M5S 1A1, Canada; (M.D.P.); (M.M.D.)
| | - Robin Chhabra
- Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
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21
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Torres F, Basaran AC, Schuller IK. Thermal Management in Neuromorphic Materials, Devices, and Networks. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205098. [PMID: 36067752 DOI: 10.1002/adma.202205098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/30/2022] [Indexed: 06/15/2023]
Abstract
Machine learning has experienced unprecedented growth in recent years, often referred to as an "artificial intelligence revolution." Biological systems inspire the fundamental approach for this new computing paradigm: using neural networks to classify large amounts of data into sorting categories. Current machine-learning schemes implement simulated neurons and synapses on standard computers based on a von Neumann architecture. This approach is inefficient in energy consumption, and thermal management, motivating the search for hardware-based systems that imitate the brain. Here, the present state of thermal management of neuromorphic computing technology and the challenges and opportunities of the energy-efficient implementation of neuromorphic devices are considered. The main features of brain-inspired computing and quantum materials for implementing neuromorphic devices are briefly described, the brain criticality and resistive switching-based neuromorphic devices are discussed, the energy and electrical considerations for spiking-based computation are presented, the fundamental features of the brain's thermal regulation are addressed, the physical mechanisms for thermal management and thermoelectric control of materials and neuromorphic devices are analyzed, and challenges and new avenues for implementing energy-efficient computing are described.
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Affiliation(s)
- Felipe Torres
- Physics Department, Faculty of Science, University of Chile, 653, Santiago, 7800024, Chile
- Center of Nanoscience and Nanotechnology (CEDENNA), Av. Ecuador 3493, Santiago, 9170124, Chile
| | - Ali C Basaran
- Department of Physics and Center for Advanced Nanoscience, University of California San Diego, La Jolla, CA, 92093, USA
| | - Ivan K Schuller
- Department of Physics and Center for Advanced Nanoscience, University of California San Diego, La Jolla, CA, 92093, USA
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22
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Park J, Ha S, Yu T, Neftci E, Cauwenberghs G. A 22-pJ/spike 73-Mspikes/s 130k-compartment neural array transceiver with conductance-based synaptic and membrane dynamics. Front Neurosci 2023; 17:1198306. [PMID: 37700751 PMCID: PMC10493285 DOI: 10.3389/fnins.2023.1198306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 07/07/2023] [Indexed: 09/14/2023] Open
Abstract
Neuromorphic cognitive computing offers a bio-inspired means to approach the natural intelligence of biological neural systems in silicon integrated circuits. Typically, such circuits either reproduce biophysical neuronal dynamics in great detail as tools for computational neuroscience, or abstract away the biology by simplifying the functional forms of neural computation in large-scale systems for machine intelligence with high integration density and energy efficiency. Here we report a hybrid which offers biophysical realism in the emulation of multi-compartmental neuronal network dynamics at very large scale with high implementation efficiency, and yet with high flexibility in configuring the functional form and the network topology. The integrate-and-fire array transceiver (IFAT) chip emulates the continuous-time analog membrane dynamics of 65 k two-compartment neurons with conductance-based synapses. Fired action potentials are registered as address-event encoded output spikes, while the four types of synapses coupling to each neuron are activated by address-event decoded input spikes for fully reconfigurable synaptic connectivity, facilitating virtual wiring as implemented by routing address-event spikes externally through synaptic routing table. Peak conductance strength of synapse activation specified by the address-event input spans three decades of dynamic range, digitally controlled by pulse width and amplitude modulation (PWAM) of the drive voltage activating the log-domain linear synapse circuit. Two nested levels of micro-pipelining in the IFAT architecture improve both throughput and efficiency of synaptic input. This two-tier micro-pipelining results in a measured sustained peak throughput of 73 Mspikes/s and overall chip-level energy efficiency of 22 pJ/spike. Non-uniformity in digitally encoded synapse strength due to analog mismatch is mitigated through single-point digital offset calibration. Combined with the flexibly layered and recurrent synaptic connectivity provided by hierarchical address-event routing of registered spike events through external memory, the IFAT lends itself to efficient large-scale emulation of general biophysical spiking neural networks, as well as rate-based mapping of rectified linear unit (ReLU) neural activations.
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Affiliation(s)
- Jongkil Park
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
- Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
- Department of Electrical and Computer Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States
| | - Sohmyung Ha
- Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
- Department of Bioengineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States
- Division of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Theodore Yu
- Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
- Department of Electrical and Computer Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States
| | - Emre Neftci
- Peter Grünberg Institute, Forschungszentrum Jülich, RWTH, Aachen, Germany
| | - Gert Cauwenberghs
- Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
- Department of Bioengineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States
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23
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Zhou K, Jia Z, Zhou Y, Ding G, Ma XQ, Niu W, Han ST, Zhao J, Zhou Y. Covalent Organic Frameworks for Neuromorphic Devices. J Phys Chem Lett 2023; 14:7173-7192. [PMID: 37540588 DOI: 10.1021/acs.jpclett.3c01711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2023]
Abstract
Neuromorphic computing could enable the potential to break the inherent limitations of conventional von Neumann architectures, which has led to widespread research interest in developing novel neuromorphic memory devices, such as memristors and bioinspired artificial synaptic devices. Covalent organic frameworks (COFs), as crystalline porous polymers, have tailorable skeletons and pores, providing unique platforms for the interplay with photons, excitons, electrons, holes, ions, spins, and molecules. Such features encourage the rising research interest in COF materials in neuromorphic electronics. To develop high-performance COF-based neuromorphic memory devices, it is necessary to comprehensively understand materials, devices, and applications. Therefore, this Perspective focuses on discussing the use of COF materials for neuromorphic memory devices in terms of molecular design, thin-film processing, and neuromorphic applications. Finally, we provide an outlook for future directions and potential applications of COF-based neuromorphic electronics.
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Affiliation(s)
- Kui Zhou
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Ziqi Jia
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Yao Zhou
- College of Materials Science and Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Guanglong Ding
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Xin-Qi Ma
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Wenbiao Niu
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Su-Ting Han
- College of Electronics and Information Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Jiyu Zhao
- State Key Laboratory of Fine Chemicals, Dalian University of Technology, 2 Linggong Road, Dalian 116024, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
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24
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Pau LF, Borza P. Quantum computing architectures with signaling and control mimicking biological processes. Heliyon 2023; 9:e18593. [PMID: 37576268 PMCID: PMC10413076 DOI: 10.1016/j.heliyon.2023.e18593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 07/20/2023] [Accepted: 07/21/2023] [Indexed: 08/15/2023] Open
Abstract
Earlier reports have described a quantum computing architecture, in which key elements are derived from control functions in biology. In this further continuing research, focus is on the signaling and control of a flow of qubits in that architecture, mimicking synapse signals and neurological controls. After a short description of that architecture, and of quantum sensing elements, it is first shown how the coloring of quantum particle flows, implemented as in mathematical colored algebras, can reduce decoherence and enhance the decidability of quantum processing elements. Next, after reviewing specific human biology functions, and exploiting experimental results on excitation modes in live animals, it is shown how to achieve separation of the quantum control & signaling signals. Technologies and designs from particle physics are discussed as well as open research issues towards a realization of a quantum computing architecture with decidable signaling.
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Affiliation(s)
- L.-F. Pau
- CBS, Copenhagen, Denmark
- Upgötva AB, Stockholm, Sweden
- Sophia Antipolis, France
| | - P.N. Borza
- Transylvania University, Brasov, Romania
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25
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McDonnell KJ. Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise. J Clin Med 2023; 12:4830. [PMID: 37510945 PMCID: PMC10381436 DOI: 10.3390/jcm12144830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.
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Affiliation(s)
- Kevin J McDonnell
- Center for Precision Medicine, Department of Medical Oncology & Therapeutics Research, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA
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26
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Schegolev AE, Klenov NV, Gubochkin GI, Kupriyanov MY, Soloviev II. Bio-Inspired Design of Superconducting Spiking Neuron and Synapse. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:2101. [PMID: 37513112 PMCID: PMC10383304 DOI: 10.3390/nano13142101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023]
Abstract
The imitative modelling of processes in the brain of living beings is an ambitious task. However, advances in the complexity of existing hardware brain models are limited by their low speed and high energy consumption. A superconducting circuit with Josephson junctions closely mimics the neuronal membrane with channels involved in the operation of the sodium-potassium pump. The dynamic processes in such a system are characterised by a duration of picoseconds and an energy level of attojoules. In this work, two superconducting models of a biological neuron are studied. New modes of their operation are identified, including the so-called bursting mode, which plays an important role in biological neural networks. The possibility of switching between different modes in situ is shown, providing the possibility of dynamic control of the system. A synaptic connection that mimics the short-term potentiation of a biological synapse is developed and demonstrated. Finally, the simplest two-neuron chain comprising the proposed bio-inspired components is simulated, and the prospects of superconducting hardware biosimilars are briefly discussed.
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Affiliation(s)
- Andrey E Schegolev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Nikolay V Klenov
- Faculty of Physics, Moscow State University, 119991 Moscow, Russia
- Faculty of Physics, Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
| | - Georgy I Gubochkin
- Faculty of Physics, Moscow State University, 119991 Moscow, Russia
- Russian Quantum Center, 100 Novaya Street, Skolkovo, 143025 Moscow, Russia
| | - Mikhail Yu Kupriyanov
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Igor I Soloviev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
- Faculty of Physics, Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
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Kim D, Choi J, Cheon M, Jeong Y, Kim J, Kwak JY, Park JK, Lee S, Kim I, Park J. Real-time Neural Connectivity Inference with Presynaptic Spike-driven Spike Timing-Dependent Plasticity. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082930 DOI: 10.1109/embc40787.2023.10341017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Brain-like artificial intelligence in electronics can be built efficiently by understanding the connectivity of neuronal circuitry. The concept of neural connectivity inference with a two-dimensional cross-bar structure memristor array is indicated in recent studies; however, large-scale implementation is challenging owing to device variations and the requirement of online parameter adaptation. This study proposes a neural connectivity inference method with one-dimensional spiking neurons using spike timing-dependent plasticity and presynaptic spike-driven spike timing-dependent plasticity learning rules, designed for a large-scale neuromorphic system. The proposed learning process decreases the number of spiking neurons by half. We simulate 12 ground-truth neural networks comprising one-dimensional eight and 64 neurons. We analyze the correlation between the neural connectivity of the ground truth and spiking neural networks using the Matthews correlation coefficient. In addition, we analyze the sensitivity and specificity of inference. Validation using the presynaptic spike-driven spike timing-dependent plasticity learning rule implies a potential approach for large-scale neural network inference with real hardware realization of large-scale neuromorphic systems.
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28
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Yuan R, Tiw PJ, Cai L, Yang Z, Liu C, Zhang T, Ge C, Huang R, Yang Y. A neuromorphic physiological signal processing system based on VO 2 memristor for next-generation human-machine interface. Nat Commun 2023; 14:3695. [PMID: 37344448 DOI: 10.1038/s41467-023-39430-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 06/08/2023] [Indexed: 06/23/2023] Open
Abstract
Physiological signal processing plays a key role in next-generation human-machine interfaces as physiological signals provide rich cognition- and health-related information. However, the explosion of physiological signal data presents challenges for traditional systems. Here, we propose a highly efficient neuromorphic physiological signal processing system based on VO2 memristors. The volatile and positive/negative symmetric threshold switching characteristics of VO2 memristors are leveraged to construct a sparse-spiking yet high-fidelity asynchronous spike encoder for physiological signals. Besides, the dynamical behavior of VO2 memristors is utilized in compact Leaky Integrate and Fire (LIF) and Adaptive-LIF (ALIF) neurons, which are incorporated into a decision-making Long short-term memory Spiking Neural Network. The system demonstrates superior computing capabilities, needing only small-sized LSNNs to attain high accuracies of 95.83% and 99.79% in arrhythmia classification and epileptic seizure detection, respectively. This work highlights the potential of memristors in constructing efficient neuromorphic physiological signal processing systems and promoting next-generation human-machine interfaces.
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Affiliation(s)
- Rui Yuan
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Pek Jun Tiw
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Lei Cai
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Zhiyu Yang
- School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China
| | - Chang Liu
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Teng Zhang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Chen Ge
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Ru Huang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Yuchao Yang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
- School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China.
- Center for Brain Inspired Chips, Institute for Artificial Intelligence, Frontiers Science Center for Nano-optoelectronics, Peking University, Beijing, 100871, China.
- Center for Brain Inspired Intelligence, Chinese Institute for Brain Research (CIBR), Beijing, Beijing, 102206, China.
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29
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López C. Artificial Intelligence and Advanced Materials. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2208683. [PMID: 36560859 DOI: 10.1002/adma.202208683] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/01/2022] [Indexed: 06/09/2023]
Abstract
Artificial intelligence (AI) is gaining strength, and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems, and processes can be devised and optimized thanks to machine learning (ML) techniques, and such progress can be turned into innovative computing platforms. Future materials scientists will profit from understanding how ML can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by computation to account for the origins, procedures, and applications of AI. ML and its methods are reviewed to provide basic knowledge of its implementation and its potential. The materials and systems used to implement AI with electric charges are finding serious competition from other information-carrying and processing agents. The impact these techniques have on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materials and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom, as exemplified by the power to discover unheard of materials or physical laws buried in data.
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Affiliation(s)
- Cefe López
- Instituto de Ciencia de Materiales de Madrid (ICMM), Consejo Superior de Investigaciones Científicas (CSIC), Calle Sor Juana Inés de la Cruz 3, Madrid, 28049, Spain
- Donostia International Physics Centre (DIPC), Paseo Manuel de Lardizábal 4, San Sebastián, 20018, España
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30
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Amin Fida A, Khanday FA, Mittal S. An active memristor based rate-coded spiking neural network. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.02.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
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31
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Bianchi S, Muñoz-Martin I, Covi E, Bricalli A, Piccolboni G, Regev A, Molas G, Nodin JF, Andrieu F, Ielmini D. A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing. Nat Commun 2023; 14:1565. [PMID: 36944647 PMCID: PMC10030830 DOI: 10.1038/s41467-023-37097-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 03/02/2023] [Indexed: 03/23/2023] Open
Abstract
Neurobiological systems continually interact with the surrounding environment to refine their behaviour toward the best possible reward. Achieving such learning by experience is one of the main challenges of artificial intelligence, but currently it is hindered by the lack of hardware capable of plastic adaptation. Here, we propose a bio-inspired recurrent neural network, mastered by a digital system on chip with resistive-switching synaptic arrays of memory devices, which exploits homeostatic Hebbian learning for improved efficiency. All the results are discussed experimentally and theoretically, proposing a conceptual framework for benchmarking the main outcomes in terms of accuracy and resilience. To test the proposed architecture for reinforcement learning tasks, we study the autonomous exploration of continually evolving environments and verify the results for the Mars rover navigation. We also show that, compared to conventional deep learning techniques, our in-memory hardware has the potential to achieve a significant boost in speed and power-saving.
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Affiliation(s)
- S Bianchi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IUNET, Milano, 20133, Italy
- Infineon Technologies, Villach, Austria
| | - I Muñoz-Martin
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IUNET, Milano, 20133, Italy
- Infineon Technologies, Villach, Austria
| | - E Covi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IUNET, Milano, 20133, Italy
- NaMLab gGmbH, Dresden, Germany
| | | | | | - A Regev
- Weebit Nano, Hod Hasharon, Israel
| | - G Molas
- Weebit Nano, Hod Hasharon, Israel
| | - J F Nodin
- Univ. Grenoble Alpes, CEA, Leti, F-38000, Grenoble, France
| | - F Andrieu
- Univ. Grenoble Alpes, CEA, Leti, F-38000, Grenoble, France
| | - D Ielmini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IUNET, Milano, 20133, Italy.
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32
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Khan MS, Olds JL. When neuro-robots go wrong: A review. Front Neurorobot 2023; 17:1112839. [PMID: 36819005 PMCID: PMC9935594 DOI: 10.3389/fnbot.2023.1112839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/19/2023] [Indexed: 02/05/2023] Open
Abstract
Neuro-robots are a class of autonomous machines that, in their architecture, mimic aspects of the human brain and cognition. As such, they represent unique artifacts created by humans based on human understanding of healthy human brains. European Union's Convention on Roboethics 2025 states that the design of all robots (including neuro-robots) must include provisions for the complete traceability of the robots' actions, analogous to an aircraft's flight data recorder. At the same time, one can anticipate rising instances of neuro-robotic failure, as they operate on imperfect data in real environments, and the underlying AI behind such neuro-robots has yet to achieve explainability. This paper reviews the trajectory of the technology used in neuro-robots and accompanying failures. The failures demand an explanation. While drawing on existing explainable AI research, we argue explainability in AI limits the same in neuro-robots. In order to make robots more explainable, we suggest potential pathways for future research.
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33
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Harikesh PC, Yang CY, Wu HY, Zhang S, Donahue MJ, Caravaca AS, Huang JD, Olofsson PS, Berggren M, Tu D, Fabiano S. Ion-tunable antiambipolarity in mixed ion-electron conducting polymers enables biorealistic organic electrochemical neurons. NATURE MATERIALS 2023; 22:242-248. [PMID: 36635590 PMCID: PMC9894750 DOI: 10.1038/s41563-022-01450-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 11/28/2022] [Indexed: 05/29/2023]
Abstract
Biointegrated neuromorphic hardware holds promise for new protocols to record/regulate signalling in biological systems. Making such artificial neural circuits successful requires minimal device/circuit complexity and ion-based operating mechanisms akin to those found in biology. Artificial spiking neurons, based on silicon-based complementary metal-oxide semiconductors or negative differential resistance device circuits, can emulate several neural features but are complicated to fabricate, not biocompatible and lack ion-/chemical-based modulation features. Here we report a biorealistic conductance-based organic electrochemical neuron (c-OECN) using a mixed ion-electron conducting ladder-type polymer with stable ion-tunable antiambipolarity. The latter is used to emulate the activation/inactivation of sodium channels and delayed activation of potassium channels of biological neurons. These c-OECNs can spike at bioplausible frequencies nearing 100 Hz, emulate most critical biological neural features, demonstrate stochastic spiking and enable neurotransmitter-/amino acid-/ion-based spiking modulation, which is then used to stimulate biological nerves in vivo. These combined features are impossible to achieve using previous technologies.
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Affiliation(s)
- Padinhare Cholakkal Harikesh
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, Norrköping, Sweden
| | - Chi-Yuan Yang
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, Norrköping, Sweden
| | - Han-Yan Wu
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, Norrköping, Sweden
| | - Silan Zhang
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, Norrköping, Sweden
- Wallenberg Wood Science Center, Linköping University, Norrköping, Sweden
| | - Mary J Donahue
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, Norrköping, Sweden
| | - April S Caravaca
- Laboratory of Immunobiology, Division of Cardiovascular Medicine, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
| | - Jun-Da Huang
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, Norrköping, Sweden
| | - Peder S Olofsson
- Laboratory of Immunobiology, Division of Cardiovascular Medicine, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
| | - Magnus Berggren
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, Norrköping, Sweden
- Wallenberg Wood Science Center, Linköping University, Norrköping, Sweden
- n-Ink AB, Norrköping, Sweden
| | - Deyu Tu
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, Norrköping, Sweden
| | - Simone Fabiano
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, Norrköping, Sweden.
- Wallenberg Wood Science Center, Linköping University, Norrköping, Sweden.
- n-Ink AB, Norrköping, Sweden.
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34
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Li Z, Tang W, Zhang B, Yang R, Miao X. Emerging memristive neurons for neuromorphic computing and sensing. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2188878. [PMID: 37090846 PMCID: PMC10120469 DOI: 10.1080/14686996.2023.2188878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.
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Affiliation(s)
- Zhiyuan Li
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
| | - Wei Tang
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
| | - Beining Zhang
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
| | - Rui Yang
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
- CONTACT Rui Yang School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan430074, China; Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Xiangshui Miao
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
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35
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Huo J, Yin H, Zhang Y, Tan X, Mao Y, Zhang C, Zhang F, Zhan G, Zhang Z, Zhang Q, Xu G, Wu Z. Quasi-Volatile MoS 2 Barristor Memory for 1T Compact Neuron by Correlative Charges Trapping and Schottky Barrier Modulation. ACS APPLIED MATERIALS & INTERFACES 2022; 14:57440-57448. [PMID: 36512440 DOI: 10.1021/acsami.2c18561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Artificial neurons as the basic units of spiking neural network (SNN) have attracted increasing interest in energy-efficient neuromorphic computing. 2D transition metal dichalcogenide (TMD)-based devices have great potential for high-performance and low-power artificial neural devices, owing to their unique ion motion, interface engineering, and resistive switching behaviors. Although there are widespread applications of TMD-based artificial synapses in neural networks, TMD-based neurons are seldom reported due to the lack of bio-plausible multi-mechanisms to mimic leaking, integrating, and firing biological behaviors without external assistance. In this work, for the first time, a methodology is developed by introducing the hybrid effect of charge trapping (CT) and Schottky barrier (SB) in MoS2 FETs for barristor memory and one-transistor (1T) compact artificial neuron realization. By correlating the CT and SB processes, quasi-volatile and resistive switching behaviors are realized on the fabricated MoS2 FET and utilized to mimic the accumulating, leaking, and firing biological behaviors of neurons. Therefore, based on a single quasi-volatile CT-SB MoS2 barristor memory, a 1T compact neuron of the basic leaky-integral-and-fire (LIF) function is demonstrated without a peripheral circuit. Furthermore, a spiking neural network (SNN) based on the CT-SB MoS2 barristor neurons is simulated and implemented in pattern classification with high accuracy approaching 95.82%. This work provides a highly integrated and inherently low-energy implementation for neural networks.
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Affiliation(s)
- Jiali Huo
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
- University of Chinese Academy of Sciences, 100049 Beijing, P. R. China
| | - Huaxiang Yin
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
- University of Chinese Academy of Sciences, 100049 Beijing, P. R. China
| | - Yadong Zhang
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
| | - Xiaosi Tan
- The National Mobile Communications Research Laboratory of Southeast University, Nanjing 211189, P. R. China
- The Purple Mountain Laboratories, Nanjing 211111, P. R. China
| | - Yunwei Mao
- The National Mobile Communications Research Laboratory of Southeast University, Nanjing 211189, P. R. China
- The Purple Mountain Laboratories, Nanjing 211111, P. R. China
| | - Chuan Zhang
- The National Mobile Communications Research Laboratory of Southeast University, Nanjing 211189, P. R. China
- The Purple Mountain Laboratories, Nanjing 211111, P. R. China
| | - Fan Zhang
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
| | - Guohui Zhan
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
- University of Chinese Academy of Sciences, 100049 Beijing, P. R. China
| | - Zhaohao Zhang
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
- University of Chinese Academy of Sciences, 100049 Beijing, P. R. China
| | - Qingzhu Zhang
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
| | - Gaobo Xu
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
| | - Zhenhua Wu
- The Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics, CAS, Beijing 100029, P. R. China
- University of Chinese Academy of Sciences, 100049 Beijing, P. R. China
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36
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Gandolfi D, Puglisi FM, Serb A, Giugliano M, Mapelli J. Editorial: Brain-inspired computing: Neuroscience drives the development of new electronics and artificial intelligence. Front Cell Neurosci 2022; 16:1115395. [PMID: 36605614 PMCID: PMC9808067 DOI: 10.3389/fncel.2022.1115395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- Daniela Gandolfi
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Francesco Maria Puglisi
- Department of Engineering “Enzo Ferrari,” University of Modena and Reggio Emilia, Modena, Italy,Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy
| | - Alexander Serb
- Centre for Electronics Frontiers, School of Engineering, University of Edinburgh, Edinburgh, United Kingdom
| | - Michele Giugliano
- Neuroscience Area, International School of Advanced Studies (SISSA), Trieste, Italy
| | - Jonathan Mapelli
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy,Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy,*Correspondence: Jonathan Mapelli ✉
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Subbulakshmi Radhakrishnan S, Chakrabarti S, Sen D, Das M, Schranghamer TF, Sebastian A, Das S. A Sparse and Spike-Timing-Based Adaptive Photoencoder for Augmenting Machine Vision for Spiking Neural Networks. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2202535. [PMID: 35674268 DOI: 10.1002/adma.202202535] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 05/31/2022] [Indexed: 06/15/2023]
Abstract
The representation of external stimuli in the form of action potentials or spikes constitutes the basis of energy efficient neural computation that emerging spiking neural networks (SNNs) aspire to imitate. With recent evidence suggesting that information in the brain is more often represented by explicit firing times of the neurons rather than mean firing rates, it is imperative to develop novel hardware that can accelerate sparse and spike-timing-based encoding. Here a medium-scale integrated circuit composed of two cascaded three-stage inverters and one XOR logic gate fabricated using a total of 21 memtransistors based on photosensitive 2D monolayer MoS2 for spike-timing-based encoding of visual information, is introduced. It is shown that different illumination intensities can be encoded into sparse spiking with time-to-first-spike representing the illumination information, that is, higher intensities invoke earlier spikes and vice versa. In addition, non-volatile and analog programmability in the photoencoder is exploited for adaptive photoencoding that allows expedited spiking under scotopic (low-light) and deferred spiking under photopic (bright-light) conditions, respectively. Finally, low energy expenditure of less than 1 µJ by the 2D-memtransistor-based photoencoder highlights the benefits of in-sensor and bioinspired design that can be transformative for the acceleration of SNNs.
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Affiliation(s)
| | - Shakya Chakrabarti
- Electrical Engineering and Computer Science, Penn State University, University Park, PA, 16802, USA
| | - Dipanjan Sen
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Mayukh Das
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Thomas F Schranghamer
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Amritanand Sebastian
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
- Electrical Engineering and Computer Science, Penn State University, University Park, PA, 16802, USA
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- Materials Research Institute, Penn State University, University Park, PA, 16802, USA
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Li H, Hu J, Chen A, Wang C, Chen L, Tian F, Zhou J, Zhao Y, Chen J, Tong Y, Loh KP, Xu Y, Zhang Y, Hasan T, Yu B. Single-Transistor Neuron with Excitatory-Inhibitory Spatiotemporal Dynamics Applied for Neuronal Oscillations. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2207371. [PMID: 36217845 DOI: 10.1002/adma.202207371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 10/02/2022] [Indexed: 06/16/2023]
Abstract
Brain-inspired neuromorphic computing systems with the potential to drive the next wave of artificial intelligence demand a spectrum of critical components beyond simple characteristics. An emerging research trend is to achieve advanced functions with ultracompact neuromorphic devices. In this work, a single-transistor neuron is demonstrated that implements excitatory-inhibitory (E-I) spatiotemporal integration and a series of essential neuron behaviors. Neuronal oscillations, the fundamental mode of neuronal communication, that construct high-dimensional population code to achieve efficient computing in the brain, can also be demonstrated by the neuron transistors. The highly scalable E-I neuron can be the basic building block for implementing core neuronal circuit motifs and large-scale architectural plans to replicate energy-efficient neural computations, forming the foundation of future integrated neuromorphic systems.
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Affiliation(s)
- Hanxi Li
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Jiayang Hu
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Anzhe Chen
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Chenhao Wang
- School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Li Chen
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Feng Tian
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
- Joint Institute of Zhejiang University and University of Illinois at Urbana-Champaign, Zhejiang University, Haining, 314400, China
| | - Jiachao Zhou
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Yuda Zhao
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Jinrui Chen
- Cambridge Graphene Centre, Cambridge University Engineering Department, Cambridge, CB3 0FA, UK
| | - Yi Tong
- Technology Development Department, Gusu Laboratory of Materials, Suzhou, 215000, China
| | - Kian Ping Loh
- Department of Chemistry, National University of Singapore, Singapore, 119077, Singapore
| | - Yang Xu
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
- Joint Institute of Zhejiang University and University of Illinois at Urbana-Champaign, Zhejiang University, Haining, 314400, China
| | - Yishu Zhang
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
| | - Tawfique Hasan
- Cambridge Graphene Centre, Cambridge University Engineering Department, Cambridge, CB3 0FA, UK
| | - Bin Yu
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 310027, China
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39
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Yan X, Qian JH, Sangwan VK, Hersam MC. Progress and Challenges for Memtransistors in Neuromorphic Circuits and Systems. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2108025. [PMID: 34813677 DOI: 10.1002/adma.202108025] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 11/07/2021] [Indexed: 06/13/2023]
Abstract
Due to the increasing importance of artificial intelligence (AI), significant recent effort has been devoted to the development of neuromorphic circuits that seek to emulate the energy-efficient information processing of the brain. While non-volatile memory (NVM) based on resistive switches, phase-change memory, and magnetic tunnel junctions has shown potential for implementing neural networks, additional multi-terminal device concepts are required for more sophisticated bio-realistic functions. Of particular interest are memtransistors based on low-dimensional nanomaterials, which are capable of electrostatically tuning memory and learning behavior at the device level. Herein, a conceptual overview of the memtransistor is provided in the context of neuromorphic circuits. Recent progress is surveyed for memtransistors and related multi-terminal NVM devices including dual-gated floating-gate memories, dual-gated ferroelectric transistors, and dual-gated van der Waals heterojunctions. The different materials systems and device architectures are classified based on the degree of control and relative tunability of synaptic behavior, with an emphasis on device concepts that harness the reduced dimensionality, weak electrostatic screening, and phase-changes properties of nanomaterials. Finally, strategies for achieving wafer-scale integration of memtransistors and multi-terminal NVM devices are delineated, with specific attention given to the materials challenges for practical neuromorphic circuits.
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Affiliation(s)
- Xiaodong Yan
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Justin H Qian
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Vinod K Sangwan
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Mark C Hersam
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208, USA
- Department of Chemistry, Northwestern University, Evanston, IL, 60208, USA
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40
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Kim S, Kim S, Ho DH, Roe DG, Choi YJ, Kim MJ, Kim UJ, Le ML, Kim J, Kim SH, Cho JH. Neurorobotic approaches to emulate human motor control with the integration of artificial synapse. SCIENCE ADVANCES 2022; 8:eabo3326. [PMID: 36170364 PMCID: PMC9519054 DOI: 10.1126/sciadv.abo3326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 08/11/2022] [Indexed: 06/16/2023]
Abstract
The advancement of electronic devices has enabled researchers to successfully emulate human synapses, thereby promoting the development of the research field of artificial synapse integrated soft robots. This paper proposes an artificial reciprocal inhibition system that can successfully emulate the human motor control mechanism through the integration of artificial synapses. The proposed system is composed of artificial synapses, load transistors, voltage/current amplifiers, and a soft actuator to demonstrate the muscle movement. The speed, range, and direction of the soft actuator movement can be precisely controlled via the preset input voltages with different amplitudes, numbers, and signs (positive or negative). The artificial reciprocal inhibition system can impart lifelike motion to soft robots and is a promising tool to enable the successful integration of soft robots or prostheses in a living body.
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Affiliation(s)
- Seonkwon Kim
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Seongchan Kim
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Dong Hae Ho
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Dong Gue Roe
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Young Jin Choi
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Min Je Kim
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Ui Jin Kim
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Manh Linh Le
- Department of Advanced Materials Engineering, Kangwon National University, Samcheok 25931, Republic of Korea
| | - Juyoung Kim
- Department of Advanced Materials Engineering, Kangwon National University, Samcheok 25931, Republic of Korea
| | - Se Hyun Kim
- Division of Chemical Engineering, Konkuk University, Seoul 05029, Republic of Korea
| | - Jeong Ho Cho
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea
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41
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Kirtas M, Oikonomou A, Passalis N, Mourgias-Alexandris G, Moralis-Pegios M, Pleros N, Tefas A. Quantization-aware training for low precision photonic neural networks. Neural Netw 2022; 155:561-573. [PMID: 36191452 DOI: 10.1016/j.neunet.2022.09.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 07/16/2022] [Accepted: 09/12/2022] [Indexed: 11/26/2022]
Abstract
Recent advances in Deep Learning (DL) fueled the interest in developing neuromorphic hardware accelerators that can improve the computational speed and energy efficiency of existing accelerators. Among the most promising research directions towards this is photonic neuromorphic architectures, which can achieve femtojoule per MAC efficiencies. Despite the benefits that arise from the use of neuromorphic architectures, a significant bottleneck is the use of expensive high-speed and precision analog-to-digital (ADCs) and digital-to-analog conversion modules (DACs) required to transfer the electrical signals, originating from the various Artificial Neural Networks (ANNs) operations (inputs, weights, etc.) in the photonic optical engines. The main contribution of this paper is to study quantization phenomena in photonic models, induced by DACs/ADCs, as an additional noise/uncertainty source and to provide a photonics-compliant framework for training photonic DL models with limited precision, allowing for reducing the need for expensive high precision DACs/ADCs. The effectiveness of the proposed method is demonstrated using different architectures, ranging from fully connected and convolutional networks to recurrent architectures, following recent advances in photonic DL.
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Affiliation(s)
- M Kirtas
- Computational Intelligence and Deep Learning Group, Dept. of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - A Oikonomou
- Computational Intelligence and Deep Learning Group, Dept. of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - N Passalis
- Computational Intelligence and Deep Learning Group, Dept. of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - G Mourgias-Alexandris
- Wireless and Photonic Systems and Networks Group, Dept. of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - M Moralis-Pegios
- Wireless and Photonic Systems and Networks Group, Dept. of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - N Pleros
- Wireless and Photonic Systems and Networks Group, Dept. of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - A Tefas
- Computational Intelligence and Deep Learning Group, Dept. of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
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42
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Lopez-Osorio P, Patiño-Saucedo A, Dominguez-Morales JP, Rostro-Gonzalez H, Perez-Peña F. Neuromorphic adaptive spiking CPG towards bio-inspired locomotion. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.06.085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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43
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Müller E, Schmitt S, Mauch C, Billaudelle S, Grübl A, Güttler M, Husmann D, Ilmberger J, Jeltsch S, Kaiser J, Klähn J, Kleider M, Koke C, Montes J, Müller P, Partzsch J, Passenberg F, Schmidt H, Vogginger B, Weidner J, Mayr C, Schemmel J. The operating system of the neuromorphic BrainScaleS-1 system. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.05.081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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44
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Ahmadi-Farsani J, Ricci S, Hashemkhani S, Ielmini D, Linares-Barranco B, Serrano-Gotarredona T. A CMOS-memristor hybrid system for implementing stochastic binary spike timing-dependent plasticity. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210018. [PMID: 35658675 PMCID: PMC9168445 DOI: 10.1098/rsta.2021.0018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 02/08/2022] [Indexed: 06/15/2023]
Abstract
This paper describes a fully experimental hybrid system in which a [Formula: see text] memristive crossbar spiking neural network (SNN) was assembled using custom high-resistance state memristors with analogue CMOS neurons fabricated in 180 nm CMOS technology. The custom memristors used NMOS selector transistors, made available on a second 180 nm CMOS chip. One drawback is that memristors operate with currents in the micro-amperes range, while analogue CMOS neurons may need to operate with currents in the pico-amperes range. One possible solution was to use a compact circuit to scale the memristor-domain currents down to the analogue CMOS neuron domain currents by at least 5-6 orders of magnitude. Here, we proposed using an on-chip compact current splitter circuit based on MOS ladders to aggressively attenuate the currents by over 5 orders of magnitude. This circuit was added before each neuron. This paper describes the proper experimental operation of an SNN circuit using a [Formula: see text] 1T1R synaptic crossbar together with four post-synaptic CMOS circuits, each with a 5-decade current attenuator and an integrate-and-fire neuron. It also demonstrates one-shot winner-takes-all training and stochastic binary spike-timing-dependent-plasticity learning using this small system. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.
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Affiliation(s)
- Javad Ahmadi-Farsani
- Instituto de Microelectrónica de Sevilla, IMSE-CNM (CSIC and Universidad de Sevilla), Av. Américo Vespucio 28, 41092 Sevilla, Spain
| | - Saverio Ricci
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy
| | - Shahin Hashemkhani
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy
| | - Daniele Ielmini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy
| | - Bernabé Linares-Barranco
- Instituto de Microelectrónica de Sevilla, IMSE-CNM (CSIC and Universidad de Sevilla), Av. Américo Vespucio 28, 41092 Sevilla, Spain
| | - Teresa Serrano-Gotarredona
- Instituto de Microelectrónica de Sevilla, IMSE-CNM (CSIC and Universidad de Sevilla), Av. Américo Vespucio 28, 41092 Sevilla, Spain
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A calibratable sensory neuron based on epitaxial VO 2 for spike-based neuromorphic multisensory system. Nat Commun 2022; 13:3973. [PMID: 35803938 PMCID: PMC9270461 DOI: 10.1038/s41467-022-31747-w] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 06/29/2022] [Indexed: 11/08/2022] Open
Abstract
Neuromorphic perception systems inspired by biology have tremendous potential in efficiently processing multi-sensory signals from the physical world, but a highly efficient hardware element capable of sensing and encoding multiple physical signals is still lacking. Here, we report a spike-based neuromorphic perception system consisting of calibratable artificial sensory neurons based on epitaxial VO2, where the high crystalline quality of VO2 leads to significantly improved cycle-to-cycle uniformity. A calibration resistor is introduced to optimize device-to-device consistency, and to adapt the VO2 neuron to different sensors with varied resistance level, a scaling resistor is further incorporated, demonstrating cross-sensory neuromorphic perception component that can encode illuminance, temperature, pressure and curvature signals into spikes. These components are utilized to monitor the curvatures of fingers, thereby achieving hand gesture classification. This study addresses the fundamental cycle-to-cycle and device-to-device variation issues of sensory neurons, therefore promoting the construction of neuromorphic perception systems for e-skin and neurorobotics.
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Sandamirskaya Y, Kaboli M, Conradt J, Celikel T. Neuromorphic computing hardware and neural architectures for robotics. Sci Robot 2022; 7:eabl8419. [PMID: 35767646 DOI: 10.1126/scirobotics.abl8419] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Neuromorphic hardware enables fast and power-efficient neural network-based artificial intelligence that is well suited to solving robotic tasks. Neuromorphic algorithms can be further developed following neural computing principles and neural network architectures inspired by biological neural systems. In this Viewpoint, we provide an overview of recent insights from neuroscience that could enhance signal processing in artificial neural networks on chip and unlock innovative applications in robotics and autonomous intelligent systems. These insights uncover computing principles, primitives, and algorithms on different levels of abstraction and call for more research into the basis of neural computation and neuronally inspired computing hardware.
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Affiliation(s)
| | - Mohsen Kaboli
- BMW Group, Department of Research, New Technologies and Innovation, Munich, Germany.,Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
| | - Jorg Conradt
- Kungliga Tekniska Högskolan (KTH), School of Electrical Engineering and Computer Science, Stockholm, Sweden
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47
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Neuromorphic object localization using resistive memories and ultrasonic transducers. Nat Commun 2022; 13:3506. [PMID: 35717413 PMCID: PMC9206646 DOI: 10.1038/s41467-022-31157-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 06/03/2022] [Indexed: 11/25/2022] Open
Abstract
Real-world sensory-processing applications require compact, low-latency, and low-power computing systems. Enabled by their in-memory event-driven computing abilities, hybrid memristive-Complementary Metal-Oxide Semiconductor neuromorphic architectures provide an ideal hardware substrate for such tasks. To demonstrate the full potential of such systems, we propose and experimentally demonstrate an end-to-end sensory processing solution for a real-world object localization application. Drawing inspiration from the barn owl’s neuroanatomy, we developed a bio-inspired, event-driven object localization system that couples state-of-the-art piezoelectric micromachined ultrasound transducer sensors to a neuromorphic resistive memories-based computational map. We present measurement results from the fabricated system comprising resistive memories-based coincidence detectors, delay line circuits, and a full-custom ultrasound sensor. We use these experimental results to calibrate our system-level simulations. These simulations are then used to estimate the angular resolution and energy efficiency of the object localization model. The results reveal the potential of our approach, evaluated in orders of magnitude greater energy efficiency than a microcontroller performing the same task. The real-world object localization application needs a low-latency and power efficient computing system. Here, Moro et al. demonstrate a neuromorphic in-memory event driven system, inspired by the barn owl’s neuroanatomy, which is orders of magnitude more energy efficient than microcontrollers.
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48
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Makarov VA, Lobov SA, Shchanikov S, Mikhaylov A, Kazantsev VB. Toward Reflective Spiking Neural Networks Exploiting Memristive Devices. Front Comput Neurosci 2022; 16:859874. [PMID: 35782090 PMCID: PMC9243340 DOI: 10.3389/fncom.2022.859874] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 05/10/2022] [Indexed: 11/29/2022] Open
Abstract
The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition. In part, it happens due to limited knowledge about the higher echelons of the brain hierarchy, where neurons actively generate predictions about what will happen next, i.e., the information processing jumps from reflex to reflection. In this study, we forecast that spiking neural networks (SNNs) can achieve the next qualitative leap. Reflective SNNs may take advantage of their intrinsic dynamics and mimic complex, not reflex-based, brain actions. They also enable a significant reduction in energy consumption. However, the training of SNNs is a challenging problem, strongly limiting their deployment. We then briefly overview new insights provided by the concept of a high-dimensional brain, which has been put forward to explain the potential power of single neurons in higher brain stations and deep SNN layers. Finally, we discuss the prospect of implementing neural networks in memristive systems. Such systems can densely pack on a chip 2D or 3D arrays of plastic synaptic contacts directly processing analog information. Thus, memristive devices are a good candidate for implementing in-memory and in-sensor computing. Then, memristive SNNs can diverge from the development of ANNs and build their niche, cognitive, or reflective computations.
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Affiliation(s)
- Valeri A. Makarov
- Instituto de Matemática Interdisciplinar, Universidad Complutense de Madrid, Madrid, Spain
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- *Correspondence: Valeri A. Makarov,
| | - Sergey A. Lobov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
- Center For Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Sergey Shchanikov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Department of Information Technologies, Vladimir State University, Vladimir, Russia
| | - Alexey Mikhaylov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Viktor B. Kazantsev
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
- Center For Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
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Abstract
Autonomous robots are expected to perform a wide range of sophisticated tasks in complex, unknown environments. However, available onboard computing capabilities and algorithms represent a considerable obstacle to reaching higher levels of autonomy, especially as robots get smaller and the end of Moore's law approaches. Here, we argue that inspiration from insect intelligence is a promising alternative to classic methods in robotics for the artificial intelligence (AI) needed for the autonomy of small, mobile robots. The advantage of insect intelligence stems from its resource efficiency (or parsimony) especially in terms of power and mass. First, we discuss the main aspects of insect intelligence underlying this parsimony: embodiment, sensory-motor coordination, and swarming. Then, we take stock of where insect-inspired AI stands as an alternative to other approaches to important robotic tasks such as navigation and identify open challenges on the road to its more widespread adoption. Last, we reflect on the types of processors that are suitable for implementing insect-inspired AI, from more traditional ones such as microcontrollers and field-programmable gate arrays to unconventional neuromorphic processors. We argue that even for neuromorphic processors, one should not simply apply existing AI algorithms but exploit insights from natural insect intelligence to get maximally efficient AI for robot autonomy.
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Affiliation(s)
- G C H E de Croon
- Micro Air Vehicle Laboratory, Faculty of Aerospace Engineering, TU Delft, Delft, Netherlands
| | - J J G Dupeyroux
- Micro Air Vehicle Laboratory, Faculty of Aerospace Engineering, TU Delft, Delft, Netherlands
| | - S B Fuller
- Autonomous Insect Robotics Laboratory, Department of Mechanical Engineering and Paul G. Allen School of Computer Science, University of Washington, Seattle, WA, USA
| | - J A R Marshall
- Opteran Technologies, Sheffield, UK
- Complex Systems Modeling Group, Department of Computer Science, University of Sheffield, Sheffield, UK
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50
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Burelo K, Sharifshazileh M, Indiveri G, Sarnthein J. Automatic Detection of High-Frequency Oscillations With Neuromorphic Spiking Neural Networks. Front Neurosci 2022; 16:861480. [PMID: 35720714 PMCID: PMC9205405 DOI: 10.3389/fnins.2022.861480] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Interictal high-frequency oscillations (HFO) detected in electroencephalography recordings have been proposed as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. Automatic HFO detectors typically analyze the data offline using complex time-consuming algorithms, which limits their clinical application. Neuromorphic circuits offer the possibility of building compact and low-power processing systems that can analyze data on-line and in real time. In this review, we describe a fully automated detection pipeline for HFO that uses, for the first time, spiking neural networks and neuromorphic technology. We demonstrated that our HFO detection pipeline can be applied to recordings from different modalities (intracranial electroencephalography, electrocorticography, and scalp electroencephalography) and validated its operation in a custom-designed neuromorphic processor. Our HFO detection approach resulted in high accuracy and specificity in the prediction of seizure outcome in patients implanted with intracranial electroencephalography and electrocorticography, and in the prediction of epilepsy severity in patients recorded with scalp electroencephalography. Our research provides a further step toward the real-time detection of HFO using compact and low-power neuromorphic devices. The real-time detection of HFO in the operation room may improve the seizure outcome of epilepsy surgery, while the use of our neuromorphic processor for non-invasive therapy monitoring might allow for more effective medication strategies to achieve seizure control. Therefore, this work has the potential to improve the quality of life in patients with epilepsy by improving epilepsy diagnostics and treatment.
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Affiliation(s)
- Karla Burelo
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zurich, Switzerland
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | | | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
- Zentrum für Neurowissenschaften Zurich, ETH und Universität Zürich, Zurich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zurich, Switzerland
- Zentrum für Neurowissenschaften Zurich, ETH und Universität Zürich, Zurich, Switzerland
- *Correspondence: Johannes Sarnthein,
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