Examining the Robustness of Spiking Neural Networks on Non-ideal Memristive Crossbars

• URL: http://arxiv.org/abs/2206.09599v1
• Date: Mon, 20 Jun 2022 07:07:41 GMT
• Title: Examining the Robustness of Spiking Neural Networks on Non-ideal Memristive Crossbars
• Authors: Abhiroop Bhattacharjee, Youngeun Kim, Abhishek Moitra, and Priyadarshini Panda
• Abstract summary: Spiking Neural Networks (SNNs) have emerged as the low-power alternative to Artificial Neural Networks (ANNs) We study the effect of crossbar non-idealities and intrinsicity on the performance of SNNs.
• Score: 4.184276171116354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking Neural Networks (SNNs) have recently emerged as the low-power alternative to Artificial Neural Networks (ANNs) owing to their asynchronous, sparse, and binary information processing. To improve the energy-efficiency and throughput, SNNs can be implemented on memristive crossbars where Multiply-and-Accumulate (MAC) operations are realized in the analog domain using emerging Non-Volatile-Memory (NVM) devices. Despite the compatibility of SNNs with memristive crossbars, there is little attention to study on the effect of intrinsic crossbar non-idealities and stochasticity on the performance of SNNs. In this paper, we conduct a comprehensive analysis of the robustness of SNNs on non-ideal crossbars. We examine SNNs trained via learning algorithms such as, surrogate gradient and ANN-SNN conversion. Our results show that repetitive crossbar computations across multiple time-steps induce error accumulation, resulting in a huge performance drop during SNN inference. We further show that SNNs trained with a smaller number of time-steps achieve better accuracy when deployed on memristive crossbars.

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