  The success of deep learning has brought forth a wave of interest in computer
hardware design to better meet the high demands of neural network inference. In
particular, analog computing hardware has been heavily motivated specifically
for accelerating neural networks, based on either electronic, optical or
photonic devices, which may well achieve lower power consumption than
conventional digital electronics. However, these proposed analog accelerators
suffer from the intrinsic noise generated by their physical components, which
makes it challenging to achieve high accuracy on deep neural networks. Hence,
for successful deployment on analog accelerators, it is essential to be able to
train deep neural networks to be robust to random continuous noise in the
network weights, which is a somewhat new challenge in machine learning. In this
paper, we advance the understanding of noisy neural networks. We outline how a
noisy neural network has reduced learning capacity as a result of loss of
mutual information between its input and output. To combat this, we propose
using knowledge distillation combined with noise injection during training to
achieve more noise robust networks, which is demonstrated experimentally across
different networks and datasets, including ImageNet. Our method achieves models
with as much as two times greater noise tolerance compared with the previous
best attempts, which is a significant step towards making analog hardware
practical for deep learning.
