Improving Noise Tolerance of Mixed-Signal Neural Networks

Understand

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.