Improving Convolution via Cache Hierarchy Tiling and Reduced Packing.

Convolution is one of the most computationally intensive machine learning model operations, usually solved by the known Im2Col + BLAS method. This work proposes a novel convolution-algorithm to improve upon Im2Col + BLAS by introducing (a) CSA: a convolution specific 3D cache-blocking analysis that focuses on tile reuse over the cache hierarchy, (b) CSO: a macro-kernel that follows CSA to compute the convolution by tiling it, (c) a specialized microkernel that seeks to achieve peak hardware performance, and (d) packing routines for the input tensor and filters to bridge the gap between tiling and micro-kernel. Our approach speeds up end-to-end machine learning model inference by up to 26% and 21% for x86 and POWER10 architectures, respectively.