SWM: A High-Performance Sparse-Winograd Matrix Multiplication CNN Accelerator

Abstract

Many convolutional neural network (CNN) accelerators are proposed to exploit the sparsity of the networks recently to enjoy the benefits of both computation and memory reduction. However, most accelerators cannot exploit the sparsity of both activations and weights. For those works that exploit both sparsity opportunities, they cannot achieve the stable load balance through a static scheduling (SS) strategy, which is vulnerable to the sparsity distribution. In this work, a balanced compressed sparse row format and a dynamic scheduling strategy are proposed to improve the load balance. A set-associate structure is also presented to tradeoff the load balance and hardware resource overhead. We propose SWM to accelerate the CNN inference, which supports both sparse convolution and sparse fully connected (FC) layers. SWM provides Winograd adaptability for large convolution kernels and supports both 16-bit and 8-bit quantized CNNs. Due to the activation sharing, 8-bit processing can achieve theoretically twice the performance of the 16-bit processing with the same sparsity. The architecture is evaluated with VGG16 and ResNet50, which achieves: at most 7.6 TOP/s for sparse-Winograd convolution and three TOP/s for sparse matrix multiplication with 16-bit quantization on Xilinx VCU1525 platform. SWM can process 310/725 images per second for VGG16/ResNet50 with 16-bit quantization. Compared with the state-of-the-art works, our design can achieve at least 1.53 × speedup and 1.8 × energy efficiency improvement.