SMART: Selective MAC zero-optimization for neural network reliability under radiation

Abstract

Neural networks running on low-power edge devices can help in achieving ubiquitous computing with limited infrastructure. When such edge devices are deployed in conventional and extreme environments without the necessary shielding, they must be fault tolerant for reliable operation. As a pilot study, we focused on embedding fault tolerance into neural networks by proposing a novel selective multiply-accumulate zero-optimization technique based on whether the value of an input provided to a neuron of a neural network is zero. If the value is zero, then the corresponding multiply-accumulate operation is bypassed. We subjected the operating system-based implementation of the optimization technique to radiation test campaigns using approximately 14 MeV neutrons, and found the proposed optimization technique to improve the fault tolerance of the tested neural network by approximately 44%.