Convolutional Neural Network Accelerator for Compression Based on Simon K-Means

Convolutional Neural Networks (CNN) are popular models widely used in image classification, target recognition, and other fields. FPGA-based accelerators for CNN are a standard method in recent years to reduce CNN's inference time and energy efficiency. However, the limitations of on-chip storage space and computing resources introduce deep compression. Contrary to most compression algorithms that pay no attention to the underlying hardware acceleration strategy and hardware-only accelerators, this paper introduces a novel model compression scheme with software and hardware collaboration for accelerating inference. First, we propose a pre-processing algorithm named Simon k-means based on clustering to quantify trained weight to speed up inference. Next, we propose a new encoding method for the quantized weight, significantly reducing the model's storage size. Finally, we give the architecture design of the accelerator using the quantized weight to accelerate the convolution. We have evaluated many popular CNNs in image classification tasks on various data sets. Experiments show that the number of multiply-accumulate operations on the convolutional layer can be reduced 66.6% with a slight loss of precision.