Selective Hardening for Neural Networks in FPGAs

Abstract

Neural networks are becoming an attractive solution for automatizing vehicles in the automotive, military, and aerospace markets. Thanks to their low-cost, low-power consumption, and flexibility, field-programmable gate arrays (FPGAs) are among the promising devices to implement neural networks. Unfortunately, FPGAs are also known to be susceptible to radiation-induced errors. In this paper, we evaluate the effects of radiation-induced errors in the output correctness of two neural networks [Iris Flower artificial neural network (ANN) and Modified National Institute of Standards and Technology (MNIST) convolutional neural network (CNN)] implemented in static random-access memory-based FPGAs. In particular, we notice that radiation can induce errors that modify the output of the network with or without affecting the neural network’s functionality. We call the former critical errors and the latter tolerable errors. Through exhaustive fault injection, we identify the portions of Iris Flower ANN and MNIST CNN implementation on FPGAs that are more likely, once corrupted, to generate a critical or a tolerable error. Based on this analysis, we propose a selective hardening strategy that triplicates only the most vulnerable layers of the neural network. With neutron radiation testing, our selective hardening solution was able to mask 40% of faults with a marginal 8% overhead in one of our tested neural networks.