COMPAC: Compressed Time-Domain, Pooling-Aware Convolution CNN Engine With Reduced Data Movement for Energy-Efficient AI Computing

Abstract

In this work, we demonstrate a compressed time-domain, pooling-aware convolution (COMPAC) convolu- tional neural network (CNN) engine for energy-efficient edge AI computing by performing multi-bit input and multi-bit weight multiply-and-accumulate (MAC) o perations in the time domain. The multi-bit inputs are compactly represented as a single pulsewidth encoded input. This translates into reduced switching capacitance (C DYN), compared with the baseline digital implemen- tation, and can enable low-power neural network computing in an edge device. COMPAC CNN engine employs a novel and an improved version of the memory delay line (MDL) supporting the time residue scaling to perform the signed accumulation of multi-bit input and multi-bit weight products in the time domain. The compressed time-domain (CTD) approach is proposed to improve the throughput in time encoding of the input activations. The simulation results of the proposed CTD approach on the AlexNet CNN over 1000 ImageNet images show that 14.71 and 7.15 input clock cycles are consumed to time-encode an 8-bit input activation in two different CTD modes, improving the throughput by 88.60% and 94.46%, respectively, compared with the conventional pulsewidth modulation-based time-domain encoding. Furthermore, a pooling-aware convolution (PAC) tech- nique is proposed to reduce the number of redundant MAC computations for the convolution layers that are followed by the max-pooling layer. The simulation results on the AlexNet