Optimizing 2D convolution for DCUs
摘要
With the growing importance of convolution in deep learning, the development of efficient convolution algorithms has become an urgent requirement. DCU(Deep Computing Unit), as an emerging GPU-like accelerator, has a relatively underdeveloped deep learning ecosystem. Therefore, this study focuses on developing efficient convolution operators for DCU. Using DCU hardware, diverse memory access patterns are created and fine thread rearrangements are performed at the warp and thread levels to optimize memory access patterns, improve computational efficiency, and improve data layout. Based on redesigned memory access patterns, the more efficient Implicit GEMM and Winograd