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Design of a Robust Memristive Spiking Neuromorphic System with Unsupervised Learning in Hardware

Authors:

Md Musabbir Adnan,

Sagarvarma Sayyaparaju,

Samuel D. Brown,

Mst Shamim Ara Shawkat,

Catherine D. Schuman, and

Garrett S. RoseAuthors Info & Claims

ACM Journal on Emerging Technologies in Computing Systems (JETC), Volume 17, Issue 4

Article No.: 56, Pages 1 - 26

https://doi.org/10.1145/3451210

Published: 30 June 2021 Publication History

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Abstract

Spiking neural networks (SNN) offer a power efficient, biologically plausible learning paradigm by encoding information into spikes. The discovery of the memristor has accelerated the progress of spiking neuromorphic systems, as the intrinsic plasticity of the device makes it an ideal candidate to mimic a biological synapse. Despite providing a nanoscale form factor, non-volatility, and low-power operation, memristors suffer from device-level non-idealities, which impact system-level performance. To address these issues, this article presents a memristive crossbar-based neuromorphic system using unsupervised learning with twin-memristor synapses, fully digital pulse width modulated spike-timing-dependent plasticity, and homeostasis neurons. The implemented single-layer SNN was applied to a pattern-recognition task of classifying handwritten-digits. The performance of the system was analyzed by varying design parameters such as number of training epochs, neurons, and capacitors. Furthermore, the impact of memristor device non-idealities, such as device-switching mismatch, aging, failure, and process variations, were investigated and the resilience of the proposed system was demonstrated.

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Cited By

Das HSchuman CChakraborty NRose G(2024)Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural NetworksScientific Reports10.1038/s41598-024-58947-214:1Online publication date: 17-Apr-2024
https://doi.org/10.1038/s41598-024-58947-2
Chakraborty NDas HRose G(2023)Homeostatic Plasticity in a Leaky Integrate and Fire Neuron Using Tunable Leak2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS)10.1109/MWSCAS57524.2023.10406066(738-742)Online publication date: 6-Aug-2023
https://doi.org/10.1109/MWSCAS57524.2023.10406066
Chakraborty NTushar SDas HRose G(2023)Energy Efficient and High-Performance Synaptic Operating Point Evaluation for SNN Applications2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS)10.1109/MWSCAS57524.2023.10406062(918-922)Online publication date: 6-Aug-2023
https://doi.org/10.1109/MWSCAS57524.2023.10406062
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Index Terms

Design of a Robust Memristive Spiking Neuromorphic System with Unsupervised Learning in Hardware
1. Hardware

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Published In

cover image ACM Journal on Emerging Technologies in Computing Systems

ACM Journal on Emerging Technologies in Computing Systems Volume 17, Issue 4

October 2021

446 pages

ISSN:1550-4832

EISSN:1550-4840

DOI:10.1145/3472280

Editor:
Ramesh Karri
Polytechnic Institute of New York University, USA

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Copyright © 2021 Association for Computing Machinery.

ACM acknowledges that this contribution was authored or co-authored by an employee, contractor, or affiliate of the United States government. As such, the United States government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for government purposes only.

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 30 June 2021

Accepted: 01 February 2021

Revised: 01 December 2020

Received: 01 May 2020

Published in JETC Volume 17, Issue 4

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U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research
Air Force Research Laboratory

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Cited By

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https://doi.org/10.1038/s41598-024-58947-2
Chakraborty NDas HRose G(2023)Homeostatic Plasticity in a Leaky Integrate and Fire Neuron Using Tunable Leak2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS)10.1109/MWSCAS57524.2023.10406066(738-742)Online publication date: 6-Aug-2023
https://doi.org/10.1109/MWSCAS57524.2023.10406066
Chakraborty NTushar SDas HRose G(2023)Energy Efficient and High-Performance Synaptic Operating Point Evaluation for SNN Applications2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS)10.1109/MWSCAS57524.2023.10406062(918-922)Online publication date: 6-Aug-2023
https://doi.org/10.1109/MWSCAS57524.2023.10406062
Shawkat MAdnan MFebbo RMurray JRose G(2023)A Single Chip SPAD Based Vision Sensing System With Integrated Memristive Spiking Neuromorphic ProcessingIEEE Access10.1109/ACCESS.2023.324479311(19441-19457)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3244793
Bouhadjar YSiegel STetzlaff TDiesmann MWaser RWouters D(2023)Sequence learning in a spiking neuronal network with memristive synapsesNeuromorphic Computing and Engineering10.1088/2634-4386/acf1c43:3(034014)Online publication date: 28-Sep-2023
https://doi.org/10.1088/2634-4386/acf1c4
Walters BJacob MAmirsoleimani ARahimi Azghadi M(2023)A Review of Graphene‐Based Memristive Neuromorphic Devices and CircuitsAdvanced Intelligent Systems10.1002/aisy.2023001365:10Online publication date: Aug-2023
https://doi.org/10.1002/aisy.202300136
Adnan MShawkat MFebbo RRose G(2022)On-Chip Interface for Event based Sensor and Spiking Neuromorphic Processing2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS)10.1109/MWSCAS54063.2022.9859274(1-4)Online publication date: 7-Aug-2022
https://doi.org/10.1109/MWSCAS54063.2022.9859274
Chakraborty NRose GKao M(2022)Programmable Refractory Period Implementations in a Mixed-Signal Integrate-And-Fire Neuron2022 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS48785.2022.9938009(770-774)Online publication date: 28-May-2022
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Xiao ZYan BZhang THuang RYang Y(2022)Memristive devices based hardware for unlabeled data processingNeuromorphic Computing and Engineering10.1088/2634-4386/ac734a2:2(022003)Online publication date: 15-Jun-2022
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Rose GShawkat MFoshie AMurray JAdnan M(2021)A system design perspective on neuromorphic computer processorsNeuromorphic Computing and Engineering10.1088/2634-4386/ac24f51:2(022001)Online publication date: 1-Nov-2021
https://doi.org/10.1088/2634-4386/ac24f5

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