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

Author:

Adnan Md Musabbir1,Sayyaparaju Sagarvarma1,Brown Samuel D.1,Shawkat Mst Shamim Ara1,Schuman Catherine D.2,Rose Garrett S.1

Affiliation:

1. University of Tennessee, Knoxville, USA

2. Oak Ridge National Laboratory, USA

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.

Funder

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

Air Force Research Laboratory

Publisher

Association for Computing Machinery (ACM)

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Software

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks;Scientific Reports;2024-04-17

2. Sequence learning in a spiking neuronal network with memristive synapses;Neuromorphic Computing and Engineering;2023-09-01

3. Energy Efficient and High-Performance Synaptic Operating Point Evaluation for SNN Applications;2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS);2023-08-06

4. Homeostatic Plasticity in a Leaky Integrate and Fire Neuron Using Tunable Leak;2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS);2023-08-06

5. A Review of Graphene‐Based Memristive Neuromorphic Devices and Circuits;Advanced Intelligent Systems;2023-08

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