Retrain-less quantization for multiplier-less convolutional neural networks

Abstract

This paper presents an approximate signed digit representation (ASD) which quantizes the weights of convolutional neural networks (CNNs) in order to make multiplier-less CNNs without performing any retraining process. Unlike the existing methods that necessitate retraining for weight quantization, the proposed method directly converts full-precision weights of CNN models into low-precision ones, attaining accuracy comparable to that of full-precision models on the Image classification tasks without going through retraining. Therefore, it is effective in saving the retraining time as well as the related computational cost. As the proposed method simplifies the weights to have up to two non-zero digits, multiplication can be realized with only add and shift operations, resulting in a speed-up of inference time and a reduction of energy consumption and hardware complexity. Experiments conducted for famous CNN architectures, such as AlexNet, VGG-16, ResNet-18 and SqueezeNet, show that the proposed method reduces the model size by 73% at the cost of a little increase of error rate, which ranges from 0.09% to 1.5% on ImageNet dataset. Compared to the previous architecture built with multipliers, the proposed multiplier-less convolution architecture reduces the critical-path delay by 52% and mitigates the hardware complexity and power consumption by more than 50%. In addition, we briefly present new floating-point quantization methods, which quantizes all parameters and feature maps of CNN models.