FKeras: A Sensitivity Analysis Tool for Edge Neural Networks

Abstract

Edge computation often requires robustness to faults, e.g., to reduce the effects of transient errors and to function correctly in high radiation environments. In these cases, the edge device must be designed with fault tolerance as a primary objective. FKeras is a tool that helps design fault-tolerant edge neural networks that run entirely on chip to meet strict latency and resource requirements. FKeras provides metrics that give a bit-level ranking of neural network weights with respect to their sensitivity to faults. FKeras includes these sensitivity metrics to guide efficient fault injection campaigns to help evaluate the robustness of a neural network architecture. We show how to use FKeras in the co-design of edge NNs trained on the high-granularity endcap calorimeter dataset, which represents high energy physics data, as well as the CIFAR-10 dataset. We use FKeras to analyze a NN’s fault tolerance to consider alongside its accuracy, performance, and resource consumption. The results show that the different NN architectures have vastly differing resilience to faults. FKeras can also determine how to protect neural network weights best, e.g., by selectively using triple modular redundancy on only the most sensitive weights, which reduces area without affecting accuracy.