Design of a Safe Convolutional Neural Network Accelerator

Recently Machine Learning (ML) accelerators have grown into prominence with significant power and performance efficiency improvements over CPU and GPU. In this paper, we developed an Algorithm Based Error Checker (ABEC) for Concurrent Error Detection (CED) based on an industry quality Convolution Neural Network (CNN) accelerator with priority to meet high safety Diagnostic Coverage (DC) requirement and enhanced area and power efficiency. Furthermore, we developed an Algorithm Based Cluster Checker (ABCC) with coarse-grained error localization to improve run-time availability. Experimental results showed that we could achieve above 99% DC with only 30% area and power overhead for a selected configuration.