  Mixed-signal hardware accelerators for deep learning achieve orders of
magnitude better power efficiency than their digital counterparts. In the
ultra-low power consumption regime, limited signal precision inherent to analog
computation becomes a challenge. We perform a case study of a 6-layer
convolutional neural network running on a mixed-signal accelerator and evaluate
its sensitivity to hardware specific noise. We apply various methods to improve
noise robustness of the network and demonstrate an effective way to optimize
useful signal ranges through adaptive signal clipping. The resulting model is
robust enough to achieve 80.2% classification accuracy on CIFAR-10 dataset with
just 1.4 mW power budget, while 6 mW budget allows us to achieve 87.1%
accuracy, which is within 1% of the software baseline. For comparison, the
unoptimized version of the same model achieves only 67.7% accuracy at 1.4 mW
and 78.6% at 6 mW.
