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

arXiv: https://arxiv.org/abs/2206.09599 · HTML: https://mlsanity.com/paper/2206.09599/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2022.

## Built on by (most cited first, 9 of 9)

- [SpikeSim: An end-to-end Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks](https://mlsanity.com/paper/2210.12899/) (2022)
- [Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing](https://mlsanity.com/paper/2305.18416/) (2023)
- [TReX- Reusing Vision Transformer's Attention for Efficient Xbar-based Computing](https://mlsanity.com/paper/2408.12742/) (2024)
- [XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars](https://mlsanity.com/paper/2302.07769/) (2023)
- [When In-memory Computing Meets Spiking Neural Networks -- A Perspective on Device-Circuit-System-and-Algorithm Co-design](https://mlsanity.com/paper/2408.12767/) (2024)
- [NEON: Enabling Efficient Support for Nonlinear Operations in Resistive RAM-based Neural Network Accelerators](https://mlsanity.com/paper/2211.05730/) (2022)
- [Security-Aware Approximate Spiking Neural Networks](https://mlsanity.com/paper/2301.05264/) (2023)
- [Low-power Spike-based Wearable Analytics on RRAM Crossbars](https://mlsanity.com/paper/2502.06736/) (2025)
- [Practical Bayesian Inference for Speech SNNs: Uncertainty and Loss-Landscape Smoothing](https://mlsanity.com/paper/2604.08624/) (2026)

## Abstract (arXiv)

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.