Implications of Noise in Resistive Memory on Deep Neural Networks for
  Image Classification
Yannick Emonds Kai Xi Holger Fröning
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.