Fast Resilient-Aware Data Layout Organization for Resistive Computing Systems

Zhang, Baogang; Murshed, M. G. Sarwar; Hussain, Faraz; Ewetz, Rickard

doi:10.1109/isvlsi49217.2020.00023

Cited by 1 publication

(1 citation statement)

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“…But, even with technological maturity, other imperfections may remain due to their inseparable relationship with the physics of the device/circuit (e.g., programming variability and interconnect resistance). Numerous methods have therefore been proposed to mitigate the effect of these non-idealities for NN applications Lim et al, 2018;He et al, 2019;Liu et al, 2020a;Wang et al, 2020b;Mahmoodi et al, 2020;Pan et al, 2020;Zhang et al, 2020;Xi et al, 2021). Although a mitigation approach can significantly increase the performance of RSM-based NNs, none of these methods are able to achieve the accuracy of their software counterparts.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Non-idealities of Resistive Switching Memories for Efficient Machine Learning

et al. 2022

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Novel computing architectures based on resistive switching memories (also known as memristors or RRAMs) have been shown to be promising approaches for tackling the energy inefficiency of deep learning and spiking neural networks. However, resistive switch technology is immature and suffers from numerous imperfections, which are often considered limitations on implementations of artificial neural networks. Nevertheless, a reasonable amount of variability can be harnessed to implement efficient probabilistic or approximate computing. This approach turns out to improve robustness, decrease overfitting and reduce energy consumption for specific applications, such as Bayesian and spiking neural networks. Thus, certain non-idealities could become opportunities if we adapt machine learning methods to the intrinsic characteristics of resistive switching memories. In this short review, we introduce some key considerations for circuit design and the most common non-idealities. We illustrate the possible benefits of stochasticity and compression with examples of well-established software methods. We then present an overview of recent neural network implementations that exploit the imperfections of resistive switching memory, and discuss the potential and limitations of these approaches.

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