Latency and accuracy optimization for binary neural network inference with locality‐aware operation skipping

Abstract

AbstractThis letter presents a novel technique to optimize latency and accuracy for the inference based on binary neural network (BNN). The effects of the spatial locality in feature maps on latency and accuracy are analyzed in the BNN inference with the previous operation‐skipping method. A regularization‐based technique is proposed to adjust the locality with the aim of further reducing latency and improving accuracy of the previous method. In the CIFAR10 classification task, 11.62% latency reduction or 0.77% accuracy increase can be achieved when optimizing for each individually. When optimizing for both simultaneously, 5.58% latency reduction and 0.59% accuracy increase can be achieved.