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Wang Y, Guo D, Jiang J, Wang H, Shang Y, Zheng J, Huang R, Li W, Wang S. Element Regulation and Dimensional Engineering Co-Optimization of Perovskite Memristors for Synaptic Plasticity Applications. ACS APPLIED MATERIALS & INTERFACES 2024. [PMID: 38422456 DOI: 10.1021/acsami.3c18053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
Capitalizing on rapid carrier migration characteristics and outstanding photoelectric conversion performance, halide perovskite memristors demonstrate an exceptional resistive switching performance. However, they have consistently faced constraints due to material stability issues. This study systematically employs elemental modulation and dimension engineering to effectively control perovskite memristors with different dimensions and A-site elements. Compared to pure 3D and 2D perovskites, the quasi-2D perovskite memristor, specifically BA0.15MA0.85PbI3, is identified as the optimal choice through observations of resistive switching (HRS current < 10-5 A, ON/OFF ratio > 103, endurance cycles > 1000, and retention time > 104 s) and synaptic plasticity characteristics. Subsequently, a comprehensive investigation into various synaptic plasticity aspects, including paired-pulse facilitation (PPF), spike-variability-dependent plasticity (SVDP), spike-rate-dependent plasticity (SRDP), and spike-timing-dependent plasticity (STDP), is conducted. Practical applications, such as memory-forgetting-memory and recognition of the Modified National Institute of Standards and Technology (MNIST) database handwritten data set (accuracy rate reaching 94.8%), are explored and successfully realized. This article provides good theoretical guidance for synaptic-like simulation in perovskite memristors.
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Affiliation(s)
- Yucheng Wang
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Dingyun Guo
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Junyu Jiang
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Hexin Wang
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yueyang Shang
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jiawei Zheng
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Ruixi Huang
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Wei Li
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shaoxi Wang
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
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Trensch G, Morrison A. A System-on-Chip Based Hybrid Neuromorphic Compute Node Architecture for Reproducible Hyper-Real-Time Simulations of Spiking Neural Networks. Front Neuroinform 2022; 16:884033. [PMID: 35846779 PMCID: PMC9277345 DOI: 10.3389/fninf.2022.884033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/23/2022] [Indexed: 11/23/2022] Open
Abstract
Despite the great strides neuroscience has made in recent decades, the underlying principles of brain function remain largely unknown. Advancing the field strongly depends on the ability to study large-scale neural networks and perform complex simulations. In this context, simulations in hyper-real-time are of high interest, as they would enable both comprehensive parameter scans and the study of slow processes, such as learning and long-term memory. Not even the fastest supercomputer available today is able to meet the challenge of accurate and reproducible simulation with hyper-real acceleration. The development of novel neuromorphic computer architectures holds out promise, but the high costs and long development cycles for application-specific hardware solutions makes it difficult to keep pace with the rapid developments in neuroscience. However, advances in System-on-Chip (SoC) device technology and tools are now providing interesting new design possibilities for application-specific implementations. Here, we present a novel hybrid software-hardware architecture approach for a neuromorphic compute node intended to work in a multi-node cluster configuration. The node design builds on the Xilinx Zynq-7000 SoC device architecture that combines a powerful programmable logic gate array (FPGA) and a dual-core ARM Cortex-A9 processor extension on a single chip. Our proposed architecture makes use of both and takes advantage of their tight coupling. We show that available SoC device technology can be used to build smaller neuromorphic computing clusters that enable hyper-real-time simulation of networks consisting of tens of thousands of neurons, and are thus capable of meeting the high demands for modeling and simulation in neuroscience.
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Affiliation(s)
- Guido Trensch
- Simulation and Data Laboratory Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, Jülich Research Centre, Jülich, Germany.,Department of Computer Science 3-Software Engineering, RWTH Aachen University, Aachen, Germany
| | - Abigail Morrison
- Simulation and Data Laboratory Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, Jülich Research Centre, Jülich, Germany.,Department of Computer Science 3-Software Engineering, RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA-Institute Brain Structure-Function Relationship (JBI-1/INM-10), Research Centre Jülich, Jülich, Germany
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Awile O, Kumbhar P, Cornu N, Dura-Bernal S, King JG, Lupton O, Magkanaris I, McDougal RA, Newton AJH, Pereira F, Săvulescu A, Carnevale NT, Lytton WW, Hines ML, Schürmann F. Modernizing the NEURON Simulator for Sustainability, Portability, and Performance. Front Neuroinform 2022; 16:884046. [PMID: 35832575 PMCID: PMC9272742 DOI: 10.3389/fninf.2022.884046] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/26/2022] [Indexed: 12/25/2022] Open
Abstract
The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.
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Affiliation(s)
- Omar Awile
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Pramod Kumbhar
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Nicolas Cornu
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Salvador Dura-Bernal
- Department Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY, United States
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - James Gonzalo King
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Olli Lupton
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Ioannis Magkanaris
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Robert A. McDougal
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States
- Yale Center for Medical Informatics, Yale University, New Haven, CT, United States
| | - Adam J. H. Newton
- Department Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY, United States
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
| | - Fernando Pereira
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Alexandru Săvulescu
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | | | - William W. Lytton
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - Michael L. Hines
- Department of Neuroscience, Yale University, New Haven, CT, United States
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
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Albers J, Pronold J, Kurth AC, Vennemo SB, Haghighi Mood K, Patronis A, Terhorst D, Jordan J, Kunkel S, Tetzlaff T, Diesmann M, Senk J. A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations. Front Neuroinform 2022; 16:837549. [PMID: 35645755 PMCID: PMC9131021 DOI: 10.3389/fninf.2022.837549] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/11/2022] [Indexed: 11/13/2022] Open
Abstract
Modern computational neuroscience strives to develop complex network models to explain dynamics and function of brains in health and disease. This process goes hand in hand with advancements in the theory of neuronal networks and increasing availability of detailed anatomical data on brain connectivity. Large-scale models that study interactions between multiple brain areas with intricate connectivity and investigate phenomena on long time scales such as system-level learning require progress in simulation speed. The corresponding development of state-of-the-art simulation engines relies on information provided by benchmark simulations which assess the time-to-solution for scientifically relevant, complementary network models using various combinations of hardware and software revisions. However, maintaining comparability of benchmark results is difficult due to a lack of standardized specifications for measuring the scaling performance of simulators on high-performance computing (HPC) systems. Motivated by the challenging complexity of benchmarking, we define a generic workflow that decomposes the endeavor into unique segments consisting of separate modules. As a reference implementation for the conceptual workflow, we develop beNNch: an open-source software framework for the configuration, execution, and analysis of benchmarks for neuronal network simulations. The framework records benchmarking data and metadata in a unified way to foster reproducibility. For illustration, we measure the performance of various versions of the NEST simulator across network models with different levels of complexity on a contemporary HPC system, demonstrating how performance bottlenecks can be identified, ultimately guiding the development toward more efficient simulation technology.
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Affiliation(s)
- Jasper Albers
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- RWTH Aachen University, Aachen, Germany
- *Correspondence: Jasper Albers
| | - Jari Pronold
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- RWTH Aachen University, Aachen, Germany
| | - Anno Christopher Kurth
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- RWTH Aachen University, Aachen, Germany
| | - Stine Brekke Vennemo
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | | | - Alexander Patronis
- Jülich Supercomputing Centre (JSC), Jülich Research Centre, Jülich, Germany
| | - Dennis Terhorst
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
| | - Jakob Jordan
- Department of Physiology, University of Bern, Bern, Switzerland
| | - Susanne Kunkel
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Tom Tetzlaff
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
| | - Markus Diesmann
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Physics, Faculty 1, RWTH Aachen University, Aachen, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, School of Medicine, RWTH Aachen University, Aachen, Germany
| | - Johanna Senk
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
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