Optimization of Direct Convolution Algorithms on ARM Processors for Deep Learning Inference

Shang Li, Fei Yu (), Shankou Zhang, Huige Yin and Hairong Lin
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Shang Li: School of Computer Science and Technology, Changsha University of Science and Technology, Changsha 410076, China
Fei Yu: School of Computer Science and Technology, Changsha University of Science and Technology, Changsha 410076, China
Shankou Zhang: School of Computer Science and Technology, Changsha University of Science and Technology, Changsha 410076, China
Huige Yin: School of Computer Science and Technology, Changsha University of Science and Technology, Changsha 410076, China
Hairong Lin: School of Electronic Information, Central South University, Changsha 410083, China

Mathematics, 2025, vol. 13, issue 5, 1-19

Abstract: In deep learning, convolutional layers typically bear the majority of the computational workload and are often the primary contributors to performance bottlenecks. The widely used convolution algorithm is based on the IM2COL transform to take advantage of the highly optimized GEMM (General Matrix Multiplication) kernel acceleration, using the highly optimized BLAS (Basic Linear Algebra Subroutine) library, which tends to incur additional memory overhead. Recent studies have indicated that direct convolution approaches can outperform traditional convolution implementations without additional memory overhead. In this paper, we propose a high-performance implementation of the direct convolution algorithm for inference that preserves the channel-first data layout of the convolutional layer inputs/outputs. We evaluate the performance of our proposed algorithm on a multi-core ARM CPU platform and compare it with state-of-the-art convolution optimization techniques. Experimental results demonstrate that our new algorithm performs better across the evaluated scenarios and platforms.

Keywords: deep learning; convolution; direct algorithm; ARMv8 architecture; performance optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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