Area and Energy Optimization for Bit-Serial Log-Quantized DNN Accelerator with Shared Accumulators

Abstract

In the remarkable evolution of deep neural network (DNN), development of a highly optimized DNN accelerator for edge computing with both less hardware resource and high computing performance is strongly required. As a well-known characteristic, DNN processing involves a large number multiplication and accumulation operations. Thus, low-precision quantization, such as binary and logarithm, is an essential technique in edge computing devices with strict restriction of circuit resource and energy. Bit-width requirement in quantization depends on application characteristics. Variable bit-width architecture based on the bit-serial processing has been proposed as a scalable alternative that allows different requirements of performance and accuracy balance by a unified hardware structure. In this paper, we propose a well-optimized DNN hardware architecture with supports of binary and variable bit-width logarithmic quantization. The key idea is the distributed-and-shared accumulator that processes multiple bit-serial inputs by a single accumulator with an additional low-overhead circuit for the binary mode. The evaluation results show that the idea reduces hardware resources by 29.8% compared to the prior architecture without losing any functionality, computing speed, and recognition accuracy. Moreover, it achieves 19.6% energy reduction using a practical DNN model of VGG 16.