Implications of Noise in Resistive Memory on Deep Neural Networks for Image Classification
This addresses the problem of reducing costs in hardware for AI practitioners by exploring noise tolerance, though it is incremental as it builds on existing memory and neural network methods.
The study investigated how much noise from resistive memory can be tolerated by convolutional neural networks in image classification on CIFAR-10, finding that networks show resilience but require countermeasures to maintain performance.
Resistive memory is a promising alternative to SRAM, but is also an inherently unstable device that requires substantial effort to ensure correct read and write operations. To avoid the associated costs in terms of area, time and energy, the present work is concerned with exploring how much noise in memory operations can be tolerated by image classification tasks based on neural networks. We introduce a special noisy operator that mimics the noise in an exemplary resistive memory unit, explore the resilience of convolutional neural networks on the CIFAR-10 classification task, and discuss a couple of countermeasures to improve this resilience.