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Resistive memory is a promising alternative to SRAM, but is also aninherently unstable device that requires substantial effort to ensure correctread and write operations. To avoid the associated costs in terms of area, timeand energy, the present work is concerned with exploring how much noise inmemory operations can be tolerated by image classification tasks based onneural networks. We introduce a special noisy operator that mimics the noise inan exemplary resistive memory unit, explore the resilience of convolutionalneural networks on the CIFAR-10 classification task, and discuss a couple ofcountermeasures to improve this resilience.

Implications of Noise in Resistive Memory on Deep Neural Networks for Image Classification