Stochastic Deep Learning in Memristive Networks

TL;DR

This paper investigates how stochastic training of deep neural networks with memristive device synapses can achieve high accuracy despite device limitations, highlighting the importance of device variability optimization.

Contribution

It demonstrates that memristive synapses with limited dynamic range and resolution can still enable effective stochastic DNN training with minimal accuracy loss.

Findings

Less than 3% accuracy loss with 15-range, 32-level memristive synapses.

Performance degradation is lower than software baseline under noisy inference.

Minimizing device variability improves noise immunity in memristive DNNs.

Abstract

We study the performance of stochastically trained deep neural networks (DNNs) whose synaptic weights are implemented using emerging memristive devices that exhibit limited dynamic range, resolution, and variability in their programming characteristics. We show that a key device parameter to optimize the learning efficiency of DNNs is the variability in its programming characteristics. DNNs with such memristive synapses, even with dynamic range as low as $15$ and only $32$ discrete levels, when trained based on stochastic updates suffer less than $3\%$ loss in accuracy compared to floating point software baseline. We also study the performance of stochastic memristive DNNs when used as inference engines with noise corrupted data and find that if the device variability can be minimized, the relative degradation in performance for the Stochastic DNN is better than that of the software baseline. Hence, our study presents a new optimization corner for memristive devices for building large noise-immune deep learning systems.