A novel zero weight/activation-aware hardware architecture of convolutional neural network

It is imperative to accelerate convolutional neural networks (CNNs) due to their ever-widening application areas from server, mobile to IoT devices. Based on the fact that CNNs can be characterized by a significant amount of zero values in both kernel weights and activations, we propose a novel hardware accelerator for CNNs exploiting zero weights and activations. We also report a zero-induced load imbalance problem, which exists in zero-aware parallel CNN hardware architectures, and present a zero-aware kernel allocation as a solution. According to our experiments with a cycle-accurate simulation model, RTL, and layout design of the proposed architecture running two real deep CNNs, pruned AlexNet 1 and VGG-16 2, our architecture offers 4x/1.8x (AlexNet) and 5.2x/2.1x (VGG-16) speedup compared with state-of-the-art zero-agnostic/zero-activation-aware architectures.