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Pashin DS, Bastrakova MV, Rybin DA, Soloviev II, Klenov NV, Schegolev AE. Optimisation Challenge for a Superconducting Adiabatic Neural Network That Implements XOR and OR Boolean Functions. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:854. [PMID: 38786810 PMCID: PMC11124324 DOI: 10.3390/nano14100854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/01/2024] [Accepted: 05/11/2024] [Indexed: 05/25/2024]
Abstract
In this article, we consider designs of simple analog artificial neural networks based on adiabatic Josephson cells with a sigmoid activation function. A new approach based on the gradient descent method is developed to adjust the circuit parameters, allowing efficient signal transmission between the network layers. The proposed solution is demonstrated on the example of a system that implements XOR and OR logical operations.
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Affiliation(s)
- Dmitrii S. Pashin
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603022 Nizhny Novgorod, Russia
| | - Marina V. Bastrakova
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603022 Nizhny Novgorod, Russia
- Russian Quantum Centre, 143025 Moscow, Russia
| | - Dmitrii A. Rybin
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603022 Nizhny Novgorod, Russia
| | - Igor. I. Soloviev
- Russian Quantum Centre, 143025 Moscow, Russia
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia;
- National University of Science and Technology MISIS, 119049 Moscow, Russia;
| | - Nikolay V. Klenov
- National University of Science and Technology MISIS, 119049 Moscow, Russia;
- Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Andrey E. Schegolev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia;
- Science Department, Moscow Technical University of Communication and Informatics (MTUCI), 111024 Moscow, Russia
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2
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Hu J, Mengu D, Tzarouchis DC, Edwards B, Engheta N, Ozcan A. Diffractive optical computing in free space. Nat Commun 2024; 15:1525. [PMID: 38378715 PMCID: PMC10879514 DOI: 10.1038/s41467-024-45982-w] [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: 09/23/2022] [Accepted: 02/09/2024] [Indexed: 02/22/2024] Open
Abstract
Structured optical materials create new computing paradigms using photons, with transformative impact on various fields, including machine learning, computer vision, imaging, telecommunications, and sensing. This Perspective sheds light on the potential of free-space optical systems based on engineered surfaces for advancing optical computing. Manipulating light in unprecedented ways, emerging structured surfaces enable all-optical implementation of various mathematical functions and machine learning tasks. Diffractive networks, in particular, bring deep-learning principles into the design and operation of free-space optical systems to create new functionalities. Metasurfaces consisting of deeply subwavelength units are achieving exotic optical responses that provide independent control over different properties of light and can bring major advances in computational throughput and data-transfer bandwidth of free-space optical processors. Unlike integrated photonics-based optoelectronic systems that demand preprocessed inputs, free-space optical processors have direct access to all the optical degrees of freedom that carry information about an input scene/object without needing digital recovery or preprocessing of information. To realize the full potential of free-space optical computing architectures, diffractive surfaces and metasurfaces need to advance symbiotically and co-evolve in their designs, 3D fabrication/integration, cascadability, and computing accuracy to serve the needs of next-generation machine vision, computational imaging, mathematical computing, and telecommunication technologies.
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Affiliation(s)
- Jingtian Hu
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Deniz Mengu
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Dimitrios C Tzarouchis
- Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Meta Materials Inc., Athens, 15123, Greece
| | - Brian Edwards
- Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Nader Engheta
- Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA.
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3
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Deb J, Saikia L, Dihingia KD, Sastry GN. ChatGPT in the Material Design: Selected Case Studies to Assess the Potential of ChatGPT. J Chem Inf Model 2024; 64:799-811. [PMID: 38237025 DOI: 10.1021/acs.jcim.3c01702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2024]
Abstract
The pursuit of designing smart and functional materials is of paramount importance across various domains, such as material science, engineering, chemical technology, electronics, biomedicine, energy, and numerous others. Consequently, researchers are actively involved in the development of innovative models and strategies for material design. Recent advancements in analytical tools, experimentation, and computer technology additionally enhance the material design possibilities. Notably, data-driven techniques like artificial intelligence and machine learning have achieved substantial progress in exploring various applications within material science. One such approach, ChatGPT, a large language model, holds transformative potential for addressing complex queries. In this article, we explore ChatGPT's understanding of material science by assigning some simple tasks across various subareas of computational material science. The findings indicate that while ChatGPT may make some minor errors in accomplishing general tasks, it demonstrates the capability to learn and adapt through human interactions. However, issues like output consistency, probable hidden errors, and ethical consequences should be addressed.
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Affiliation(s)
- Jyotirmoy Deb
- Advanced Computation and Data Sciences Division, CSIR-North East Institute of Science and Technology, Jorhat 785006, Assam, India
| | - Lakshi Saikia
- Advanced Materials Group, Materials Sciences & Technology Division, CSIR-North East Institute of Science and Technology, Jorhat 785006, Assam, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
| | - Kripa Dristi Dihingia
- Advanced Computation and Data Sciences Division, CSIR-North East Institute of Science and Technology, Jorhat 785006, Assam, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
| | - G Narahari Sastry
- Advanced Computation and Data Sciences Division, CSIR-North East Institute of Science and Technology, Jorhat 785006, Assam, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
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4
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Iliasov AI, Matsukatova AN, Emelyanov AV, Slepov PS, Nikiruy KE, Rylkov VV. Adapted MLP-Mixer network based on crossbar arrays of fast and multilevel switching (Co-Fe-B) x(LiNbO 3) 100-x nanocomposite memristors. NANOSCALE HORIZONS 2024; 9:238-247. [PMID: 38165725 DOI: 10.1039/d3nh00421j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2024]
Abstract
MLP-Mixer based on multilayer perceptrons (MLPs) is a novel architecture of a neuromorphic computing system (NCS) introduced for image classification tasks without convolutional layers. Its software realization demonstrates high classification accuracy, although the number of trainable weights is relatively low. One more promising way of improving the NCS performance, especially in terms of power consumption, is its hardware realization using memristors. Therefore, in this work, we proposed an NCS with an adapted MLP-Mixer architecture and memristive weights. For this purpose, we used a passive crossbar array of (Co-Fe-B)x(LiNbO3)100-x memristors. Firstly, we studied the characteristics of such memristors, including their minimal resistive switching time, which was extrapolated to be in the picosecond range. Secondly, we created a fully hardware NCS with memristive weights that are capable of classification of simple 4-bit vectors. The system was shown to be robust to noise introduction in the input patterns. Finally, we used experimental memristive characteristics to simulate an adapted MLP-Mixer architecture that demonstrated a classification accuracy of (94.7 ± 0.3)% on the Modified National Institute of Standards and Technology (MNIST) dataset. The obtained results are the first steps toward the realization of memristive NCS with a promising MLP-Mixer architecture.
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Affiliation(s)
- Aleksandr I Iliasov
- National Research Centre Kurchatov Institute, 123182 Moscow, Russia.
- Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Anna N Matsukatova
- National Research Centre Kurchatov Institute, 123182 Moscow, Russia.
- Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Andrey V Emelyanov
- National Research Centre Kurchatov Institute, 123182 Moscow, Russia.
- Moscow Institute of Physics and Technology (State University), 141700 Dolgoprudny, Moscow Region, Russia
| | - Pavel S Slepov
- Steklov Mathematical Institute RAS, 119991 Moscow, Russia
| | | | - Vladimir V Rylkov
- National Research Centre Kurchatov Institute, 123182 Moscow, Russia.
- Kotelnikov Institute of Radio Engineering and Electronics RAS, 141190 Fryazino, Moscow Region, Russia
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5
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Minnekhanov A, Matsukatova A, Trofimov A, Nesmelov A, Zavyalov S, Demin V, Emelyanov A. Reliable Memristive Synapses Based on Parylene-MoO x Nanocomposites for Neuromorphic Applications. ACS APPLIED MATERIALS & INTERFACES 2023; 15:54996-55008. [PMID: 37962902 DOI: 10.1021/acsami.3c13956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Memristive devices, known for their nonvolatile resistive switching, are promising components for next-generation neuromorphic computing systems, which mimic the brain's neural architecture. Specifically, these devices are well-suited for functioning as artificial synapses due to their analogue tunability and low energy consumption. However, the improvement of their performance and reliability remains a pressing challenge. In this study, we report the development and comprehensive characterization of memristive devices based on a parylene-MoOx (PPX-Mo) nanocomposite layer, which exhibit improved characteristics over their parylene-based counterparts: lower switching voltage and energy, smaller dispersion, and better resistive plasticity. A robust statistical analysis identified the optimal synthesis parameters for these devices, providing valuable insights for future device optimization. The most probable resistive switching mechanism of the devices is proposed. By successfully integrating these memristors into a neuromorphic computing model and showcasing their scalability in crossbar geometry, we demonstrate their potential as functional artificial synapses. The results obtained from this study can be useful for the development of hardware-brain-inspired computational systems.
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Affiliation(s)
| | - Anna Matsukatova
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Lomonosov Moscow State University, Moscow 119991, Russia
| | - Andrey Trofimov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
| | | | - Sergey Zavyalov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
| | - Vyacheslav Demin
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
| | - Andrey Emelyanov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
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6
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Jimenez-Mesa C, Arco JE, Martinez-Murcia FJ, Suckling J, Ramirez J, Gorriz JM. Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects. Pharmacol Res 2023; 197:106984. [PMID: 37940064 DOI: 10.1016/j.phrs.2023.106984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 10/04/2023] [Accepted: 11/04/2023] [Indexed: 11/10/2023]
Abstract
The integration of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging techniques with machine learning (ML) algorithms, including deep learning (DL) models, is a promising approach. This integration enhances the precision and efficiency of current diagnostic and treatment strategies while offering invaluable insights into disease mechanisms. In this comprehensive review, we delve into the transformative impact of ML and DL in this domain. Firstly, a brief analysis is provided of how these algorithms have evolved and which are the most widely applied in this domain. Their different potential applications in nuclear imaging are then discussed, such as optimization of image adquisition or reconstruction, biomarkers identification, multimodal fusion and the development of diagnostic, prognostic, and disease progression evaluation systems. This is because they are able to analyse complex patterns and relationships within imaging data, as well as extracting quantitative and objective measures. Furthermore, we discuss the challenges in implementation, such as data standardization and limited sample sizes, and explore the clinical opportunities and future horizons, including data augmentation and explainable AI. Together, these factors are propelling the continuous advancement of more robust, transparent, and reliable systems.
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Affiliation(s)
- Carmen Jimenez-Mesa
- Department of Signal Theory, Networking and Communications, University of Granada, 18010, Spain
| | - Juan E Arco
- Department of Signal Theory, Networking and Communications, University of Granada, 18010, Spain; Department of Communications Engineering, University of Malaga, 29010, Spain
| | | | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge CB21TN, UK
| | - Javier Ramirez
- Department of Signal Theory, Networking and Communications, University of Granada, 18010, Spain
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, University of Granada, 18010, Spain; Department of Psychiatry, University of Cambridge, Cambridge CB21TN, UK.
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7
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Vlasov D, Minnekhanov A, Rybka R, Davydov Y, Sboev A, Serenko A, Ilyasov A, Demin V. Memristor-based spiking neural network with online reinforcement learning. Neural Netw 2023; 166:512-523. [PMID: 37579580 DOI: 10.1016/j.neunet.2023.07.031] [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: 09/19/2022] [Revised: 04/28/2023] [Accepted: 07/24/2023] [Indexed: 08/16/2023]
Abstract
Neural networks implemented in memristor-based hardware can provide fast and efficient in-memory computation, but traditional learning methods such as error back-propagation are hardly feasible in it. Spiking neural networks (SNNs) are highly promising in this regard, as their weights can be changed locally in a self-organized manner without the demand for high-precision changes calculated with the use of information almost from the entire network. This problem is rather relevant for solving control tasks with neural-network reinforcement learning methods, as those are highly sensitive to any source of stochasticity in a model initialization, training, or decision-making procedure. This paper presents an online reinforcement learning algorithm in which the change of connection weights is carried out after processing each environment state during interaction-with-environment data generation. Another novel feature of the algorithm is that it is applied to SNNs with memristor-based STDP-like learning rules. The plasticity functions are obtained from real memristors based on poly-p-xylylene and CoFeB-LiNbO3 nanocomposite, which were experimentally assembled and analyzed. The SNN is comprised of leaky integrate-and-fire neurons. Environmental states are encoded by the timings of input spikes, and the control action is decoded by the first spike. The proposed learning algorithm solves the Cart-Pole benchmark task successfully. This result could be the first step towards implementing a real-time agent learning procedure in a continuous-time environment that can be run on neuromorphic systems with memristive synapses.
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Affiliation(s)
- Danila Vlasov
- NRC "Kurchatov Institute", Akademika Kurchatova sq., 1 Moscow, Russian Federation
| | - Anton Minnekhanov
- NRC "Kurchatov Institute", Akademika Kurchatova sq., 1 Moscow, Russian Federation
| | - Roman Rybka
- NRC "Kurchatov Institute", Akademika Kurchatova sq., 1 Moscow, Russian Federation; Russian Technological University "MIREA", Vernadsky av., 78 Moscow, Russian Federation.
| | - Yury Davydov
- NRC "Kurchatov Institute", Akademika Kurchatova sq., 1 Moscow, Russian Federation
| | - Alexander Sboev
- NRC "Kurchatov Institute", Akademika Kurchatova sq., 1 Moscow, Russian Federation; Russian Technological University "MIREA", Vernadsky av., 78 Moscow, Russian Federation; NRNU "MEPhi", Kashira Hwy, 31 Moscow, Russian Federation
| | - Alexey Serenko
- NRC "Kurchatov Institute", Akademika Kurchatova sq., 1 Moscow, Russian Federation
| | - Alexander Ilyasov
- NRC "Kurchatov Institute", Akademika Kurchatova sq., 1 Moscow, Russian Federation; Faculty of Physics, Lomonosov Moscow State University, Leninskie gory, 1 Moscow, Russian Federation
| | - Vyacheslav Demin
- NRC "Kurchatov Institute", Akademika Kurchatova sq., 1 Moscow, Russian Federation.
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8
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Singhal R, Saraswat V, Deshmukh S, Subramoney S, Somappa L, Baghini MS, Ganguly U. Enhanced regularization for on-chip training using analog and temporary memory weights. Neural Netw 2023; 165:1050-1057. [PMID: 37478527 DOI: 10.1016/j.neunet.2023.07.001] [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: 12/15/2022] [Revised: 05/17/2023] [Accepted: 07/02/2023] [Indexed: 07/23/2023]
Abstract
In-memory computing techniques are used to accelerate artificial neural network (ANN) training and inference tasks. Memory technology and architectural innovations allow efficient matrix-vector multiplications, gradient calculations, and updates to network weights. However, on-chip learning for edge devices is quite challenging due to the frequent updates. Here, we propose using an analog and temporary on-chip memory (ATOM) cell with controllable retention timescales for implementing the weights of an on-chip training task. Measurement results for Read-Write timescales are presented for an ATOM cell fabricated in GlobalFoundries' 45 nm RFSOI technology. The effect of limited retention and its variability is evaluated for training a fully connected neural network with a variable number of layers for the MNIST hand-written digit recognition task. Our studies show that weight decay due to temporary memory can have benefits equivalent to regularization, achieving a ∼33% reduction in the validation error (from 3.6% to 2.4%). We also show that the controllability of the decay timescale can be advantageous in achieving a further ∼26% reduction in the validation error. This strongly suggests the utility of temporary memory during learning before on-chip non-volatile memories can take over for the storage and inference tasks using the neural network weights. We thus propose an algorithm-circuit codesign in the form of temporary analog memory for high-performing on-chip learning of ANNs.
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Affiliation(s)
- Raghav Singhal
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India.
| | - Vivek Saraswat
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Shreyas Deshmukh
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | | | - Laxmeesha Somappa
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Maryam Shojaei Baghini
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Udayan Ganguly
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India
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9
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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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10
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Kasperek D, Antonowicz P, Baranowski M, Sokolowska M, Podpora M. Comparison of the Usability of Apple M2 and M1 Processors for Various Machine Learning Tasks. SENSORS (BASEL, SWITZERLAND) 2023; 23:5424. [PMID: 37420589 PMCID: PMC10305298 DOI: 10.3390/s23125424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/06/2023] [Accepted: 06/06/2023] [Indexed: 07/09/2023]
Abstract
Thispaper compares the usability of various Apple MacBook Pro laptops were tested for basic machine learning research applications, including text-based, vision-based, and tabular data. Four tests/benchmarks were conducted using four different MacBook Pro models-M1, M1 Pro, M2, and M2 Pro. A script written in Swift was used to train and evaluate four machine learning models using the Create ML framework, and the process was repeated three times. The script also measured performance metrics, including time results. The results were presented in tables, allowing for a comparison of the performance of each device and the impact of their hardware architectures.
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Affiliation(s)
- David Kasperek
- Department of Computer Science, Opole University of Technology, Proszkowska 76, 45-758 Opole, Poland (M.P.)
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11
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Cucchi M, Parker D, Stavrinidou E, Gkoupidenis P, Kleemann H. In Liquido Computation with Electrochemical Transistors and Mixed Conductors for Intelligent Bioelectronics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2209516. [PMID: 36813270 DOI: 10.1002/adma.202209516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 12/22/2022] [Indexed: 06/18/2023]
Abstract
Next-generation implantable computational devices require long-term-stable electronic components capable of operating in, and interacting with, electrolytic surroundings without being damaged. Organic electrochemical transistors (OECTs) emerged as fitting candidates. However, while single devices feature impressive figures of merit, integrated circuits (ICs) immersed in common electrolytes are hard to realize using electrochemical transistors, and there is no clear path forward for optimal top-down circuit design and high-density integration. The simple observation that two OECTs immersed in the same electrolytic medium will inevitably interact hampers their implementation in complex circuitry. The electrolyte's ionic conductivity connects all the devices in the liquid, producing unwanted and often unforeseeable dynamics. Minimizing or harnessing this crosstalk has been the focus of very recent studies. Herein, the main challenges, trends, and opportunities for realizing OECT-based circuitry in a liquid environment that could circumnavigate the hard limits of engineering and human physiology, are discussed. The most successful approaches in autonomous bioelectronics and information processing are analyzed. Elaborating on the strategies to circumvent and harness device crosstalk proves that platforms capable of complex computation and even machine learning (ML) can be realized in liquido using mixed ionic-electronic conductors (OMIECs).
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Affiliation(s)
- Matteo Cucchi
- Ecole Polytechnique Fédérale de Lausanne (EPFL), Laboratory for Soft Bioelectronic Interfaces, Neuro-X Institute, Chemin des Mines 9, Geneva, 1202, Switzerland
- Dresden Integrated Center for Applied Photophysics and Photonic Materials (IAPP), Technische Universität Dresden, Helmholtzstr. 1, 01187, Dresden, Germany
| | - Daniela Parker
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, Norrköping, SE-60174, Sweden
| | - Eleni Stavrinidou
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, Norrköping, SE-60174, Sweden
| | | | - Hans Kleemann
- Dresden Integrated Center for Applied Photophysics and Photonic Materials (IAPP), Technische Universität Dresden, Helmholtzstr. 1, 01187, Dresden, Germany
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12
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Surianarayanan C, Lawrence JJ, Chelliah PR, Prakash E, Hewage C. Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3062. [PMID: 36991773 PMCID: PMC10053494 DOI: 10.3390/s23063062] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.
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Affiliation(s)
| | | | | | - Edmond Prakash
- Research Center for Creative Arts, University for the Creative Arts (UCA), Farnham GU9 7DS, UK
| | - Chaminda Hewage
- Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
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13
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Buckley SM, Tait AN, McCaughan AN, Shastri BJ. Photonic online learning: a perspective. NANOPHOTONICS 2023; 12:833-845. [PMID: 36909290 PMCID: PMC9995662 DOI: 10.1515/nanoph-2022-0553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/31/2022] [Accepted: 12/03/2022] [Indexed: 06/18/2023]
Abstract
Emerging neuromorphic hardware promises to solve certain problems faster and with higher energy efficiency than traditional computing by using physical processes that take place at the device level as the computational primitives in neural networks. While initial results in photonic neuromorphic hardware are very promising, such hardware requires programming or "training" that is often power-hungry and time-consuming. In this article, we examine the online learning paradigm, where the machinery for training is built deeply into the hardware itself. We argue that some form of online learning will be necessary if photonic neuromorphic hardware is to achieve its true potential.
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Affiliation(s)
- Sonia Mary Buckley
- Applied Physics Division, National Institute of Standards and Technology, Boulder, CO80305, USA
| | - Alexander N. Tait
- Department of Physics, Engineering Physics and Astronomy, Queen’s University, Kingston, ON, Canada
| | - Adam N. McCaughan
- Applied Physics Division, National Institute of Standards and Technology, Boulder, CO80305, USA
| | - Bhavin J. Shastri
- Department of Physics, Engineering Physics and Astronomy, Queen’s University, Kingston, ON, Canada
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14
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Fu X, Li T, Cai B, Miao J, Panin GN, Ma X, Wang J, Jiang X, Li Q, Dong Y, Hao C, Sun J, Xu H, Zhao Q, Xia M, Song B, Chen F, Chen X, Lu W, Hu W. Graphene/MoS 2-xO x/graphene photomemristor with tunable non-volatile responsivities for neuromorphic vision processing. LIGHT, SCIENCE & APPLICATIONS 2023; 12:39. [PMID: 36750548 PMCID: PMC9905593 DOI: 10.1038/s41377-023-01079-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/06/2023] [Accepted: 01/13/2023] [Indexed: 06/18/2023]
Abstract
Conventional artificial intelligence (AI) machine vision technology, based on the von Neumann architecture, uses separate sensing, computing, and storage units to process huge amounts of vision data generated in sensory terminals. The frequent movement of redundant data between sensors, processors and memory, however, results in high-power consumption and latency. A more efficient approach is to offload some of the memory and computational tasks to sensor elements that can perceive and process the optical signal simultaneously. Here, we proposed a non-volatile photomemristor, in which the reconfigurable responsivity can be modulated by the charge and/or photon flux through it and further stored in the device. The non-volatile photomemristor has a simple two-terminal architecture, in which photoexcited carriers and oxygen-related ions are coupled, leading to a displaced and pinched hysteresis in the current-voltage characteristics. For the first time, non-volatile photomemristors implement computationally complete logic with photoresponse-stateful operations, for which the same photomemristor serves as both a logic gate and memory, using photoresponse as a physical state variable instead of light, voltage and memresistance. The polarity reversal of photomemristors shows great potential for in-memory sensing and computing with feature extraction and image recognition for neuromorphic vision.
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Affiliation(s)
- Xiao Fu
- School of Physics and Optoelectronic Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Tangxin Li
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Bin Cai
- Institute of Intelligent Machines, HFIPS, Chinese Academy of Sciences, Hefei, 230031, China
- Jianghuai Frontier Technology Coordination and Innovation Center, Hefei, 230088, China
| | - Jinshui Miao
- School of Physics and Optoelectronic Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China.
- University of Chinese Academy of Sciences, 100049, Beijing, China.
| | - Gennady N Panin
- Institute of Microelectronics Technology and High-Purity Materials, Russian Academy of Sciences, Chernogolovka, Moscow district, 142432, Russia
| | - Xinyu Ma
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Jinjin Wang
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Xiaoyong Jiang
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Qing Li
- School of Physics and Optoelectronic Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Yi Dong
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Chunhui Hao
- School of Physics and Optoelectronic Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Juyi Sun
- School of Physics and Optoelectronic Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Hangyu Xu
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Qixiao Zhao
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Mengjia Xia
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Bo Song
- Institute of Intelligent Machines, HFIPS, Chinese Academy of Sciences, Hefei, 230031, China.
- Jianghuai Frontier Technology Coordination and Innovation Center, Hefei, 230088, China.
| | - Fansheng Chen
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Xiaoshuang Chen
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Wei Lu
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Weida Hu
- School of Physics and Optoelectronic Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China.
- University of Chinese Academy of Sciences, 100049, Beijing, China.
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15
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Zhang L, Chan M. Editorial: Hardware implementation of spike-based neuromorphic computing and its design methodologies. Front Neurosci 2023; 16:1113983. [PMID: 36685232 PMCID: PMC9854259 DOI: 10.3389/fnins.2022.1113983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 12/05/2022] [Indexed: 01/07/2023] Open
Affiliation(s)
- Lining Zhang
- School of Electronic and Computer Engineering, Peking University, Shenzhen, China,*Correspondence: Lining Zhang ✉
| | - Mansun Chan
- Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong SAR, China,ACCESS-AI Chip Center for Emerging Smart Systems, InnoHK Centers, HKSP, Hong Kong, Hong Kong SAR, China
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16
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Zhou W, Wen S, Liu Y, Liu L, Liu X, Chen L. Forgetting memristor based STDP learning circuit for neural networks. Neural Netw 2023; 158:293-304. [PMID: 36493532 DOI: 10.1016/j.neunet.2022.11.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 10/18/2022] [Accepted: 11/14/2022] [Indexed: 11/21/2022]
Abstract
The circuit implementation of STDP based on memristor is of great significance for the application of neural network. However, recent research shows that the research on the pure circuit implementation of forgetting memristor and STDP is still rare. This paper proposes a new STDP learning rule implementation circuit based on the forgetting memristor. This kind of forgetting memory resistance synapse makes the neural network have the function of time-division multiplexing, but the instability of short-term memory will affect the learning ability of the neural network. This paper analyzes and discusses the influence of synapses with long-term and short-term memory on the learning characteristics of neural network STDP, which lays a foundation for the construction of time-division multiplexing neural network with long-term and short-term memory synapses. Through this circuit, it is found that the volatile memristor has different behaviors to the stimulus signal in different initial states, and the resulting LTP phenomenon is more in line with the forgetting effect in biology. This circuit has multiple adjustable parameters, which can fit the STDP learning rules under different conditions. The application of neural network proves the availability of this circuit.
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Affiliation(s)
- Wenhao Zhou
- Electronic Information and Engineering, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, 400715, China.
| | - Shiping Wen
- Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.
| | - Yi Liu
- Electronic Information and Engineering, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, 400715, China
| | - Lu Liu
- Electronic Information and Engineering, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, 400715, China
| | - Xin Liu
- Computer Vision and Pattern Recognition Laboratory, School of Engineering Science, Lappeenranta-Lahti University of Technology LUT, Finland.
| | - Ling Chen
- Electronic Information and Engineering, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, 400715, China; Computer Vision and Pattern Recognition Laboratory, School of Engineering Science, Lappeenranta-Lahti University of Technology LUT, Finland.
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17
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Milano G, Miranda E, Fretto M, Valov I, Ricciardi C. Experimental and Modeling Study of Metal-Insulator Interfaces to Control the Electronic Transport in Single Nanowire Memristive Devices. ACS APPLIED MATERIALS & INTERFACES 2022; 14:53027-53037. [PMID: 36396122 PMCID: PMC9716557 DOI: 10.1021/acsami.2c11022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 08/25/2022] [Indexed: 06/16/2023]
Abstract
Memristive devices relying on redox-based resistive switching mechanisms represent promising candidates for the development of novel computing paradigms beyond von Neumann architecture. Recent advancements in understanding physicochemical phenomena underlying resistive switching have shed new light on the importance of an appropriate selection of material properties required to optimize the performance of devices. However, despite great attention has been devoted to unveiling the role of doping concentration, impurity type, adsorbed moisture, and catalytic activity at the interfaces, specific studies concerning the effect of the counter electrode in regulating the electronic flow in memristive cells are scarce. In this work, the influence of the metal-insulator Schottky interfaces in electrochemical metallization memory (ECM) memristive cell model systems based on single-crystalline ZnO nanowires (NWs) is investigated following a combined experimental and modeling approach. By comparing and simulating the electrical characteristics of single NW devices with different contact configurations and by considering Ag and Pt electrodes as representative of electrochemically active and inert electrodes, respectively, we highlight the importance of an appropriate choice of electrode materials by taking into account the Schottky barrier height and interface chemistry at the metal-insulator interfaces. In particular, we show that a clever choice of metal-insulator interfaces allows to reshape the hysteretic conduction characteristics of the device and to increase the device performance by tuning its resistance window. These results obtained from single NW-based devices provide new insights into the selection criteria for materials and interfaces in connection with the design of advanced ECM cells.
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Affiliation(s)
- Gianluca Milano
- Advanced
Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135Torino, Italy
| | - Enrique Miranda
- Departament
d’Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193Cerdanyola del Vallès, Spain
| | - Matteo Fretto
- Advanced
Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135Torino, Italy
| | - Ilia Valov
- JARA—Fundamentals
for Future Information Technology, 52425Jülich, Germany
- Peter-Grünberg-Institut
(PGI 7), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52425Jülich, Germany
| | - Carlo Ricciardi
- Department
of Applied Science and Technology, Politecnico
di Torino, C.so Duca degli Abruzzi 24, 10129Torino, Italy
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18
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Liu F, Deswal S, Christou A, Sandamirskaya Y, Kaboli M, Dahiya R. Neuro-inspired electronic skin for robots. Sci Robot 2022; 7:eabl7344. [PMID: 35675450 DOI: 10.1126/scirobotics.abl7344] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most of the state-of-the-art robots today and suggests the need for neural-like hardware for electronic skin (e-skin). This could be attained through either innovative schemes for developing distributed electronics or repurposing the neuromorphic circuits developed for other sensory modalities such as vision and audio. This Review highlights the hardware implementations of various computational building blocks for e-skin and the ways they can be integrated to potentially realize human skin-like or peripheral nervous system-like functionalities. The neural-like sensing and data processing are discussed along with various algorithms and hardware architectures. The integration of ultrathin neuromorphic chips for local computation and the printed electronics on soft substrate used for the development of e-skin over large areas are expected to advance robotic interaction as well as open new avenues for research in medical instrumentation, wearables, electronics, and neuroprosthetics.
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Affiliation(s)
- Fengyuan Liu
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Sweety Deswal
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Adamos Christou
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | | | - Mohsen Kaboli
- Department of Research, New Technologies, Innovation, BMW Group, Parkring 19, 85748 Garching bei Munchen, Germany.,Cognitive Robotics and Tactile Intelligence Group, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Ravinder Dahiya
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
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19
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Chen J, Zhu C, Cao G, Liu H, Bian R, Wang J, Li C, Chen J, Fu Q, Liu Q, Meng P, Li W, Liu F, Liu Z. Mimicking Neuroplasticity via Ion Migration in van der Waals Layered Copper Indium Thiophosphate. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2104676. [PMID: 34652030 DOI: 10.1002/adma.202104676] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/30/2021] [Indexed: 06/13/2023]
Abstract
Artificial synaptic devices are the essential components of neuromorphic computing systems, which are capable of parallel information storage and processing with high area and energy efficiencies, showing high promise in future storage systems and in-memory computing. Analogous to the diffusion of neurotransmitter between neurons, ion-migration-based synaptic devices are becoming promising for mimicking synaptic plasticity, though the precise control of ion migration is still challenging. Due to the unique 2D nature and highly anisotropic ionic transport properties, van der Waals layered materials are attractive for synaptic device applications. Here, utilizing the high conductivity from Cu+ -ion migration, a two-terminal artificial synaptic device based on layered copper indium thiophosphate is studied. By controlling the migration of Cu+ ions with an electric field, the device mimics various neuroplasticity functions, such as short-term plasticity, long-term plasticity, and spike-time-dependent plasticity. The Pavlovian conditioning and activity-dependent synaptic plasticity involved neural functions are also successfully emulated. These results show a promising opportunity to modulate ion migration in 2D materials through field-driven ionic processes, making the demonstrated synaptic device an intriguing candidate for future low-power neuromorphic applications.
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Affiliation(s)
- Jiangang Chen
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313099, China
| | - Chao Zhu
- School of Materials Science and Engineering, Nanyang Technological University, BLK N4.1, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Guiming Cao
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Haishi Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Renji Bian
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Jinyong Wang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Changcun Li
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Jieqiong Chen
- School of Materials Science and Engineering, Nanyang Technological University, BLK N4.1, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Qundong Fu
- School of Materials Science and Engineering, Nanyang Technological University, BLK N4.1, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Qing Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Peng Meng
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Wei Li
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Fucai Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313099, China
| | - Zheng Liu
- School of Materials Science and Engineering, Nanyang Technological University, BLK N4.1, 50 Nanyang Avenue, Singapore, 639798, Singapore
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
- CINTRA CNRS/NTU/THALES, Research Techno Plaza, UMI 3288, Singapore, 637553, Singapore
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20
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Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse. Nat Commun 2022; 13:2811. [PMID: 35589710 PMCID: PMC9120471 DOI: 10.1038/s41467-022-30432-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 04/25/2022] [Indexed: 12/02/2022] Open
Abstract
Neuromorphic computing targets the hardware embodiment of neural network, and device implementation of individual neuron and synapse has attracted considerable attention. The emulation of synaptic plasticity has shown promising results after the advent of memristors. However, neuronal intrinsic plasticity, which involves in learning process through interactions with synaptic plasticity, has been rarely demonstrated. Synaptic and intrinsic plasticity occur concomitantly in learning process, suggesting the need of the simultaneous implementation. Here, we report a neurosynaptic device that mimics synaptic and intrinsic plasticity concomitantly in a single cell. Threshold switch and phase change memory are merged in threshold switch-phase change memory device. Neuronal intrinsic plasticity is demonstrated based on bottom threshold switch layer, which resembles the modulation of firing frequency in biological neuron. Synaptic plasticity is also introduced through the nonvolatile switching of top phase change layer. Intrinsic and synaptic plasticity are simultaneously emulated in a single cell to establish the positive feedback between them. A positive feedback learning loop which mimics the retraining process in biological system is implemented in threshold switch-phase change memory array for accelerated training. Synaptic plasticity and neuronal intrinsic plasticity are both involved in the learning process of hardware artificial neural network. Here, Lee et al. integrate a threshold switch and a phase change memory in a single device, which emulates biological synaptic and intrinsic plasticity simultaneously.
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21
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Shu H, Long H, Sun H, Li B, Zhang H, Wang X. Dynamic Model of the Short-Term Synaptic Behaviors of PEDOT-based Organic Electrochemical Transistors with Modified Shockley Equations. ACS OMEGA 2022; 7:14622-14629. [PMID: 35557652 PMCID: PMC9088794 DOI: 10.1021/acsomega.1c06864] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 04/07/2022] [Indexed: 06/15/2023]
Abstract
Neuromorphic computing is an emerging area with prospects to break the energy efficiency bottleneck of artificial intelligence (AI). A crucial challenge for neuromorphic computing is understanding the working principles of artificial synaptic devices. As an emerging class of synaptic devices, organic electrochemical transistors (OECTs) have attracted significant interest due to ultralow voltage operation, analog conductance tuning, mechanical flexibility, and biocompatibility. However, little work has been focused on the first-principal modeling of the synaptic behaviors of OECTs. The simulation of OECT synaptic behaviors is of great importance to understanding the OECT working principles as neuromorphic devices and optimizing ultralow power consumption neuromorphic computing devices. Here, we develop a two-dimensional transient drift-diffusion model based on modified Shockley equations for poly(3,4-ethylenedioxythiophene) (PEDOT)-based OECTs. We reproduced the typical transistor characteristics of these OECTs including the unique non-monotonic transconductance-gate bias curve and frequency dependency of transconductance. Furthermore, typical synaptic phenomena, such as excitatory/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation/depression (PPF/PPD), and short-term plasticity (STP), are also demonstrated. This work is crucial in guiding the experimental exploration of neuromorphic computing devices and has the potential to serve as a platform for future OECT device simulation based on a wide range of semiconducting materials.
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Affiliation(s)
- Haonian Shu
- Department
of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Ave, Columbus, Ohio 43210, United States
| | - Haowei Long
- School
of Materials Science and Engineering, Zhejiang
University, Hangzhou, Zhejiang 310027, P. R. China
| | - Haibin Sun
- Department
of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Ave, Columbus, Ohio 43210, United States
| | - Baochen Li
- Department
of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Ave, Columbus, Ohio 43210, United States
| | - Haomiao Zhang
- State
Key Laboratory of Chemical Engineering, College of Chemical and Biological
Engineering, Zhejiang University, Hangzhou 310027, P. R. China
| | - Xiaoxue Wang
- Department
of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Ave, Columbus, Ohio 43210, United States
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22
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A 3D-printed neuromorphic humanoid hand for grasping unknown objects. iScience 2022; 25:104119. [PMID: 35391826 PMCID: PMC8980759 DOI: 10.1016/j.isci.2022.104119] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 01/21/2022] [Accepted: 03/16/2022] [Indexed: 11/21/2022] Open
Abstract
Compared with conventional von Neumann's architecture-based processors, neuromorphic systems provide energy-saving in-memory computing. We present here a 3D neuromorphic humanoid hand designed for providing an artificial unconscious response based on training. The neuromorphic humanoid hand system mimics the reflex arc for a quick response by managing complex spatiotemporal information. A 3D structural humanoid hand is first integrated with 3D-printed pressure sensors and a portable neuromorphic device that was fabricated by the multi-axis robot 3D printing technology. The 3D neuromorphic robot hand provides bioinspired signal perception, including detection, signal transmission, and signal processing, together with the biomimetic reflex arc function, allowing it to hold an unknown object with an automatically increased gripping force without a conventional controlling processor. The proposed system offers a new approach for realizing an unconscious response with an artificially intelligent robot.
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23
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Deiana AM, Tran N, Agar J, Blott M, Di Guglielmo G, Duarte J, Harris P, Hauck S, Liu M, Neubauer MS, Ngadiuba J, Ogrenci-Memik S, Pierini M, Aarrestad T, Bähr S, Becker J, Berthold AS, Bonventre RJ, Müller Bravo TE, Diefenthaler M, Dong Z, Fritzsche N, Gholami A, Govorkova E, Guo D, Hazelwood KJ, Herwig C, Khan B, Kim S, Klijnsma T, Liu Y, Lo KH, Nguyen T, Pezzullo G, Rasoulinezhad S, Rivera RA, Scholberg K, Selig J, Sen S, Strukov D, Tang W, Thais S, Unger KL, Vilalta R, von Krosigk B, Wang S, Warburton TK. Applications and Techniques for Fast Machine Learning in Science. Front Big Data 2022; 5:787421. [PMID: 35496379 PMCID: PMC9041419 DOI: 10.3389/fdata.2022.787421] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 01/31/2020] [Indexed: 01/10/2023] Open
Abstract
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science-the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.
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Affiliation(s)
| | - Nhan Tran
- Fermi National Accelerator Laboratory, Batavia, IL, United States
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Joshua Agar
- Department of Materials Science and Engineering, Lehigh University, Bethlehem, PA, United States
| | | | | | - Javier Duarte
- Department of Physics, University of California, San Diego, San Diego, CA, United States
| | - Philip Harris
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Scott Hauck
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Mia Liu
- Department of Physics and Astronomy, Purdue University, West Lafayette, IN, United States
| | - Mark S. Neubauer
- Department of Physics, University of Illinois Urbana-Champaign, Champaign, IL, United States
| | | | - Seda Ogrenci-Memik
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Maurizio Pierini
- European Organization for Nuclear Research (CERN), Meyrin, Switzerland
| | - Thea Aarrestad
- European Organization for Nuclear Research (CERN), Meyrin, Switzerland
| | - Steffen Bähr
- Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Jürgen Becker
- Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Anne-Sophie Berthold
- Institute of Nuclear and Particle Physics, Technische Universität Dresden, Dresden, Germany
| | | | - Tomás E. Müller Bravo
- Department of Physics and Astronomy, University of Southampton, Southampton, United Kingdom
| | - Markus Diefenthaler
- Thomas Jefferson National Accelerator Facility, Newport News, VA, United States
| | - Zhen Dong
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
| | - Nick Fritzsche
- Institute of Nuclear and Particle Physics, Technische Universität Dresden, Dresden, Germany
| | - Amir Gholami
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
| | | | - Dongning Guo
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | | | - Christian Herwig
- Fermi National Accelerator Laboratory, Batavia, IL, United States
| | - Babar Khan
- Department of Computer Science, Technical University Darmstadt, Darmstadt, Germany
| | - Sehoon Kim
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
| | - Thomas Klijnsma
- Fermi National Accelerator Laboratory, Batavia, IL, United States
| | - Yaling Liu
- Department of Bioengineering, Lehigh University, Bethlehem, PA, United States
| | - Kin Ho Lo
- Department of Physics, University of Florida, Gainesville, FL, United States
| | - Tri Nguyen
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | | | | | - Ryan A. Rivera
- Fermi National Accelerator Laboratory, Batavia, IL, United States
| | - Kate Scholberg
- Department of Physics, Duke University, Durham, NC, United States
| | | | - Sougata Sen
- Birla Institute of Technology and Science, Pilani, India
| | - Dmitri Strukov
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, United States
| | - William Tang
- Department of Physics, Princeton University, Princeton, NJ, United States
| | - Savannah Thais
- Department of Physics, Princeton University, Princeton, NJ, United States
| | | | - Ricardo Vilalta
- Department of Computer Science, University of Houston, Houston, TX, United States
| | - Belina von Krosigk
- Karlsruhe Institute of Technology, Karlsruhe, Germany
- Department of Physics, Universität Hamburg, Hamburg, Germany
| | - Shen Wang
- Department of Physics, University of Florida, Gainesville, FL, United States
| | - Thomas K. Warburton
- Department of Physics and Astronomy, Iowa State University, Ames, IA, United States
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24
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Shvetsov BS, Minnekhanov AA, Emelyanov AV, Ilyasov AI, Grishchenko YV, Zanaveskin ML, Nesmelov AA, Streltsov DR, Patsaev TD, Vasiliev AL, Rylkov VV, Demin VA. Parylene-based memristive crossbar structures with multilevel resistive switching for neuromorphic computing. NANOTECHNOLOGY 2022; 33:255201. [PMID: 35276689 DOI: 10.1088/1361-6528/ac5cfe] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 03/11/2022] [Indexed: 06/14/2023]
Abstract
Currently, there is growing interest in wearable and biocompatible smart computing and information processing systems that are safe for the human body. Memristive devices are promising for solving such problems due to a number of their attractive properties, such as low power consumption, scalability, and the multilevel nature of resistive switching (plasticity). The multilevel plasticity allows memristors to emulate synapses in hardware neuromorphic computing systems (NCSs). The aim of this work was to study Cu/poly-p-xylylene(PPX)/Au memristive elements fabricated in the crossbar geometry. In developing the technology for manufacturing such samples, we took into account their characteristics, in particular stable and multilevel resistive switching (at least 10 different states) and low operating voltage (<2 V), suitable for NCSs. Experiments on cycle to cycle (C2C) switching of a single memristor and device to device (D2D) switching of several memristors have shown high reproducibility of resistive switching (RS) voltages. Based on the obtained memristors, a formal hardware neuromorphic network was created that can be trained to classify simple patterns.
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Affiliation(s)
- Boris S Shvetsov
- National Research Centre 'Kurchatov Institute', 123182, Moscow, Russia
| | | | - Andrey V Emelyanov
- National Research Centre 'Kurchatov Institute', 123182, Moscow, Russia
- Moscow Institute of Physics and Technology, 141700, Dolgoprudny, Moscow Region, Russia
| | - Aleksandr I Ilyasov
- National Research Centre 'Kurchatov Institute', 123182, Moscow, Russia
- Lomonosov Moscow State University, 119991, Moscow, Russia
| | | | | | | | | | - Timofey D Patsaev
- National Research Centre 'Kurchatov Institute', 123182, Moscow, Russia
| | - Alexander L Vasiliev
- National Research Centre 'Kurchatov Institute', 123182, Moscow, Russia
- Moscow Institute of Physics and Technology, 141700, Dolgoprudny, Moscow Region, Russia
- Federal Research Center 'Crystallography and Photonics' of the Russian Academy of Sciences, 117342, Moscow, Russia
| | - Vladimir V Rylkov
- National Research Centre 'Kurchatov Institute', 123182, Moscow, Russia
- Kotel'nikov Institute of Radio Engineering and Electronics RAS, 141190, Fryazino, Moscow Region, Russia
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25
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Zheng J, Xue X, Ji C, Yuan Y, Sun K, Rosenmann D, Wang L, Wu J, Campbell JC, Guha S. Dynamic-quenching of a single-photon avalanche photodetector using an adaptive resistive switch. Nat Commun 2022; 13:1517. [PMID: 35314686 PMCID: PMC8938474 DOI: 10.1038/s41467-022-29195-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 02/22/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractOne of the most common approaches for quenching single-photon avalanche diodes is to use a passive resistor in series with it. A drawback of this approach has been the limited recovery speed of the single-photon avalanche diodes. High resistance is needed to quench the avalanche, leading to slower recharging of the single-photon avalanche diodes depletion capacitor. We address this issue by replacing a fixed quenching resistor with a bias-dependent adaptive resistive switch. Reversible generation of metallic conduction enables switching between low and high resistance states under unipolar bias. As an example, using a Pt/Al2O3/Ag resistor with a commercial silicon single-photon avalanche diodes, we demonstrate avalanche pulse widths as small as ~30 ns, 10× smaller than a passively quenched approach, thus significantly improving the single-photon avalanche diodes frequency response. The experimental results are consistent with a model where the adaptive resistor dynamically changes its resistance during discharging and recharging the single-photon avalanche diodes.
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26
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Chen S, Valov I. Design of Materials Configuration for Optimizing Redox-Based Resistive Switching Memories. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2105022. [PMID: 34695257 DOI: 10.1002/adma.202105022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 10/19/2021] [Indexed: 06/13/2023]
Abstract
Redox-based resistive random access memories (ReRAMs) are based on electrochemical processes of oxidation and reduction within the devices. The selection of materials and material combinations strongly influence the related nanoscale processes, playing a crucial role in resistive switching properties and functionalities. To date, however, comprehensive studies on device design accounting for a combination of factors such as electrodes, electrolytes, and capping layer materials related to their thicknesses and interactions are scarce. In this work, the impact of materials' configuration on interfacial redox reactions in HfO2 -based electrochemical metallization memory (ECM) and valence-change memory (VCM) systems is reported. The redox processes are studied by cyclic voltammetry, and the corresponding resistive switching characteristics are investigated. In ECM cells, the overall cell resistance depends on the electrocatalytic activity of the counter electrode. Nonetheless, the capping layer material further influences the cell resistance and the SET and RESET voltages. In VCM systems, the influence of the electrode material configuration is also pronounced, and is capable of modulating the active resistive switching interface. For both types of memory cells, the switching behavior changes significantly with variation of the oxide thickness. The results present important materials selection criteria for rationale design of ReRAM cells for various memristive applications.
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Affiliation(s)
- Shaochuan Chen
- Institut für Werkstoffe der Elektrotechnik II, RWTH Aachen University, Sommerfeldstraße 24, 52074, Aachen, Germany
| | - Ilia Valov
- Institut für Werkstoffe der Elektrotechnik II, RWTH Aachen University, Sommerfeldstraße 24, 52074, Aachen, Germany
- Peter Grünberg Institut 7 and JARA-FIT, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
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27
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A Novel In-Sensor Computing Architecture Based on Single Photon Avalanche Diode and Dynamic Memristor. ARTIF INTELL 2022. [DOI: 10.1007/978-3-031-20503-3_39] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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28
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Kiani F, Yin J, Wang Z, Yang JJ, Xia Q. A fully hardware-based memristive multilayer neural network. SCIENCE ADVANCES 2021; 7:eabj4801. [PMID: 34818038 DOI: 10.1126/sciadv.abj4801] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Memristive crossbar arrays promise substantial improvements in computing throughput and power efficiency through in-memory analog computing. Previous machine learning demonstrations with memristive arrays, however, relied on software or digital processors to implement some critical functionalities, leading to frequent analog/digital conversions and more complicated hardware that compromises the energy efficiency and computing parallelism. Here, we show that, by implementing the activation function of a neural network in analog hardware, analog signals can be transmitted to the next layer without unnecessary digital conversion, communication, and processing. We have designed and built compact rectified linear units, with which we constructed a two-layer perceptron using memristive crossbar arrays, and demonstrated a recognition accuracy of 93.63% for the Modified National Institute of Standard and Technology (MNIST) handwritten digits dataset. The fully hardware-based neural network reduces both the data shuttling and conversion, capable of delivering much higher computing throughput and power efficiency.
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Affiliation(s)
- Fatemeh Kiani
- Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Jun Yin
- Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Zhongrui Wang
- Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Qiangfei Xia
- Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA 01003, USA
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29
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Cucchi M, Gruener C, Petrauskas L, Steiner P, Tseng H, Fischer A, Penkovsky B, Matthus C, Birkholz P, Kleemann H, Leo K. Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification. SCIENCE ADVANCES 2021; 7:7/34/eabh0693. [PMID: 34407948 PMCID: PMC8373129 DOI: 10.1126/sciadv.abh0693] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 06/28/2021] [Indexed: 05/12/2023]
Abstract
Early detection of malign patterns in patients' biological signals can save millions of lives. Despite the steady improvement of artificial intelligence-based techniques, the practical clinical application of these methods is mostly constrained to an offline evaluation of the patients' data. Previous studies have identified organic electrochemical devices as ideal candidates for biosignal monitoring. However, their use for pattern recognition in real time was never demonstrated. Here, we produce and characterize brain-inspired networks composed of organic electrochemical transistors and use them for time-series predictions and classification tasks using the reservoir computing approach. To show their potential use for biofluid monitoring and biosignal analysis, we classify four classes of arrhythmic heartbeats with an accuracy of 88%. The results of this study introduce a previously unexplored paradigm for biocompatible computational platforms and may enable development of ultralow-power consumption hardware-based artificial neural networks capable of interacting with body fluids and biological tissues.
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Affiliation(s)
- Matteo Cucchi
- Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), Nöthnitzer Str. 61, 01187 Dresden, Germany.
| | - Christopher Gruener
- Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), Nöthnitzer Str. 61, 01187 Dresden, Germany
| | - Lautaro Petrauskas
- Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), Nöthnitzer Str. 61, 01187 Dresden, Germany
- Chair for Circuit Design and Network Theory (CCN), Technische Universität Dresden, Helmholtzstr. 18, 01069 Dresden, Germany
| | - Peter Steiner
- Institute for Acoustics and Speech Communication (IAS), Technische Universität Dresden, Helmholtzstr. 18, 01069 Dresden, Germany
| | - Hsin Tseng
- Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), Nöthnitzer Str. 61, 01187 Dresden, Germany
| | - Axel Fischer
- Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), Nöthnitzer Str. 61, 01187 Dresden, Germany
| | - Bogdan Penkovsky
- National University of Kyiv-Mohyla Academy, Skovorody Str. 2, 04655 Kyiv, Ukraine
- Alysophil SAS, Bio Parc, 850 Boulevard Sebastien Brant, BP 30170 F, 67405, Illkirch CEDEX, France
| | - Christian Matthus
- Chair for Circuit Design and Network Theory (CCN), Technische Universität Dresden, Helmholtzstr. 18, 01069 Dresden, Germany
| | - Peter Birkholz
- Institute for Acoustics and Speech Communication (IAS), Technische Universität Dresden, Helmholtzstr. 18, 01069 Dresden, Germany
| | - Hans Kleemann
- Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), Nöthnitzer Str. 61, 01187 Dresden, Germany
| | - Karl Leo
- Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), Nöthnitzer Str. 61, 01187 Dresden, Germany
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30
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Energy-Efficient Non-Von Neumann Computing Architecture Supporting Multiple Computing Paradigms for Logic and Binarized Neural Networks. JOURNAL OF LOW POWER ELECTRONICS AND APPLICATIONS 2021. [DOI: 10.3390/jlpea11030029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Different in-memory computing paradigms enabled by emerging non-volatile memory technologies are promising solutions for the development of ultra-low-power hardware for edge computing. Among these, SIMPLY, a smart logic-in-memory architecture, provides high reconfigurability and enables the in-memory computation of both logic operations and binarized neural networks (BNNs) inference. However, operation-specific hardware accelerators can result in better performance for a particular task, such as the analog computation of the multiply and accumulate operation for BNN inference, but lack reconfigurability. Nonetheless, a solution providing the flexibility of SIMPLY while also achieving the high performance of BNN-specific analog hardware accelerators is missing. In this work, we propose a novel in-memory architecture based on 1T1R crossbar arrays, which enables the coexistence on the same crossbar array of both SIMPLY computing paradigm and the analog acceleration of the multiply and accumulate operation for BNN inference. We also highlight the main design tradeoffs and opportunities enabled by different emerging non-volatile memory technologies. Finally, by using a physics-based Resistive Random Access Memory (RRAM) compact model calibrated on data from the literature, we show that the proposed architecture improves the energy delay product by >103 times when performing a BNN inference task with respect to a SIMPLY implementation.
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31
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Covi E, Donati E, Liang X, Kappel D, Heidari H, Payvand M, Wang W. Adaptive Extreme Edge Computing for Wearable Devices. Front Neurosci 2021; 15:611300. [PMID: 34045939 PMCID: PMC8144334 DOI: 10.3389/fnins.2021.611300] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 03/24/2021] [Indexed: 11/13/2022] Open
Abstract
Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.
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Affiliation(s)
| | - Elisa Donati
- Institute of Neuroinformatics, University of Zurich, Eidgenössische Technische Hochschule Zürich (ETHZ), Zurich, Switzerland
| | - Xiangpeng Liang
- Microelectronics Lab, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | - David Kappel
- Bernstein Center for Computational Neuroscience, III Physikalisches Institut–Biophysik, Georg-August Universität, Göttingen, Germany
| | - Hadi Heidari
- Microelectronics Lab, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | - Melika Payvand
- Institute of Neuroinformatics, University of Zurich, Eidgenössische Technische Hochschule Zürich (ETHZ), Zurich, Switzerland
| | - Wei Wang
- The Andrew and Erna Viterbi Department of Electrical Engineering, Technion–Israel Institute of Technology, Haifa, Israel
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32
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Recent Advances in Sequential Infiltration Synthesis (SIS) of Block Copolymers (BCPs). NANOMATERIALS 2021; 11:nano11040994. [PMID: 33924480 PMCID: PMC8069880 DOI: 10.3390/nano11040994] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 04/01/2021] [Accepted: 04/04/2021] [Indexed: 12/15/2022]
Abstract
In the continuous downscaling of device features, the microelectronics industry is facing the intrinsic limits of conventional lithographic techniques. The development of new synthetic approaches for large-scale nanopatterned materials with enhanced performances is therefore required in the pursuit of the fabrication of next-generation devices. Self-assembled materials as block copolymers (BCPs) provide great control on the definition of nanopatterns, promising to be ideal candidates as templates for the selective incorporation of a variety of inorganic materials when combined with sequential infiltration synthesis (SIS). In this review, we report the latest advances in nanostructured inorganic materials synthesized by infiltration of self-assembled BCPs. We report a comprehensive description of the chemical and physical characterization techniques used for in situ studies of the process mechanism and ex situ measurements of the resulting properties of infiltrated polymers. Finally, emerging optical and electrical properties of such materials are discussed.
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