ComPEND

Abstract

While negative inputs for ReLU are useless, it consumes a lot of computing power to calculate them for deep neural networks. We propose a computation pruning technique that detects at an early stage that the result of a sum of products will be negative by adopting an inverted two's complement expression for weights and a bit-serial sum of products. Therefore, it can skip a large amount of computations for negative results and simply set the ReLU outputs to zero. Moreover, we devise a DNN accelerator architecture that can efficiently apply the proposed technique. The evaluation shows that the accelerator using the computation pruning through early negative detection technique significantly improves the energy efficiency and the performance.