A Context-Awareness and Hardware-Friendly Sparse Matrix Multiplication Kernel for CNN Inference Acceleration

Abstract

Sparsification technology is crucial for deploying convolutional neural networks in resource-constrained environments. However, the efficiency of sparse models is hampered by irregular memory access patterns in sparse matrix multiplication kernels. Hardware-level support for 2:4 granularity in sparse tensor cores presents an opportunity for designing efficient sparse matrix multiplication kernels. Existing approaches often involve adjusting sparse structures or secondary sparsification, introducing additional computational errors. To tackle this challenge, we introduce a flexible 2:4 structured adaptive sparse matrix multiplication (FS-AMM) method, a hardware-friendly sparse matrix multiplication kernel that leverages model context to accelerate convolutional neural networks. First, we propose a model context-aware matrix pre-processing method that employs heuristic algorithms to estimate a loss of accuracy due to weight sparsity at each layer. Second, we design a hardware-friendly sparse storage format that combines 2:4 sparse and dense storage formats, enabling more versatile sparsity ratio selection. Third, we implement efficient matrix multiplication kernels to optimize GPU utilization. Finally, experimental results on A100 GPUs show that our method effectively utilizes the sparse tensor kernel and obtains an average 3.09 times speedup ratio compared to other sparse methods while maintaining a high accuracy.