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The Winograd or Cook-Toom class of algorithms help to reduce the overallcompute complexity of many modern deep convolutional neural networks (CNNs).Although there has been a lot of research done on model and algorithmicoptimization of CNN, little attention has been paid to the efficientimplementation of these algorithms on embedded CPUs, which usually have verylimited memory and low power budget. This paper aims to fill this gap andfocuses on the efficient implementation of Winograd or Cook-Toom basedconvolution on modern Arm Cortex-A CPUs, widely used in mobile devices today.Specifically, we demonstrate a reduction in inference latency by using a set ofoptimization strategies that improve the utilization of computationalresources, and by effectively leveraging the ARMv8-A NEON SIMD instruction set.We evaluated our proposed region-wise multi-channel implementations on ArmCortex-A73 platform using several representative CNNs. The results showsignificant performance improvements in full network, up to 60%, over existingim2row/im2col based optimization techniques

Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs