Accurate neuron resilience prediction for a flexible reliability management in neural network accelerators

摘要

Deep neural networks have become a ubiquitous tool for mastering complex classification tasks. Current research focuses on the development of power-efficient and fast neural network hardware accelerators for mobile and embedded devices. However, when used in safety-critical applications, for example autonomously operating vehicles, the reliability of such accelerators becomes a further optimization criterion which can stand in contrast to power-efficiency and latency. Furthermore, ensuring hardware reliability becomes increasingly challenging for shrinking structure widths and rising power densities in the nanometer semiconductor technology era. One solution to this challenge is the exploitation of fault tolerant parts in deep neural networks. In this paper we propose a new method for predicting the error resilience of neurons in deep neural networks and show that this method significantly improves upon existing methods in terms of accuracy as well as interpretability. We evaluate prediction accuracy by simulating hardware faults in networks trained on the CIFAR-10 and ILSVRC image classification benchmarks and protecting neurons according to the resilience estimations. In addition, we demonstrate how our resilience prediction can be used for a flexible trade-off between reliability and efficiency in neural network hardware accelerators.