Advancing Direct Convolution using Convolution Slicing Optimization and ISA Extensions

• URL: http://arxiv.org/abs/2303.04739v1
• Date: Wed, 8 Mar 2023 17:23:39 GMT
• Title: Advancing Direct Convolution using Convolution Slicing Optimization and ISA Extensions
• Authors: Victor Ferrari, Rafael Sousa, Marcio Pereira, Jo\~ao P. L. de Carvalho, Jos\'e Nelson Amaral, Jos\'e Moreira, Guido Araujo
• Abstract summary: Convolution is one of the most computationally intensive operations that must be performed for machine-learning model inference. This paper proposes SConv: a direct-convolution algorithm based on a MLIR/LLVM code-generation toolchain that can be integrated into machine-learning compilers.
• Score: 1.2006896500048552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolution is one of the most computationally intensive operations that must be performed for machine-learning model inference. A traditional approach to compute convolutions is known as the Im2Col + BLAS method. This paper proposes SConv: a direct-convolution algorithm based on a MLIR/LLVM code-generation toolchain that can be integrated into machine-learning compilers . This algorithm introduces: (a) Convolution Slicing Analysis (CSA) - a convolution-specific 3D cache-blocking analysis pass that focuses on tile reuse over the cache hierarchy; (b) Convolution Slicing Optimization (CSO) - a code-generation pass that uses CSA to generate a tiled direct-convolution macro-kernel; and (c) Vector-Based Packing (VBP) - an architecture-specific optimized input-tensor packing solution based on vector-register shift instructions for convolutions with unitary stride. Experiments conducted on 393 convolutions from full ONNX-MLIR machine-learning models indicate that the elimination of the Im2Col transformation and the use of fast packing routines result in a total packing time reduction, on full model inference, of 2.0x - 3.9x on Intel x86 and 3.6x - 7.2x on IBM POWER10. The speed-up over an Im2Col + BLAS method based on current BLAS implementations for end-to-end machine-learning model inference is in the range of 9% - 25% for Intel x86 and 10% - 42% for IBM POWER10 architectures. The total convolution speedup for model inference is 12% - 27% on Intel x86 and 26% - 46% on IBM POWER10. SConv also outperforms BLAS GEMM, when computing pointwise convolutions, in more than 83% of the 219 tested instances.

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