  The Winograd or Cook-Toom class of algorithms help to reduce the overall
compute complexity of many modern deep convolutional neural networks (CNNs).
Although there has been a lot of research done on model and algorithmic
optimization of CNN, little attention has been paid to the efficient
implementation of these algorithms on embedded CPUs, which usually have very
limited memory and low power budget. This paper aims to fill this gap and
focuses on the efficient implementation of Winograd or Cook-Toom based
convolution on modern Arm Cortex-A CPUs, widely used in mobile devices today.
Specifically, we demonstrate a reduction in inference latency by using a set of
optimization strategies that improve the utilization of computational
resources, and by effectively leveraging the ARMv8-A NEON SIMD instruction set.
We evaluated our proposed region-wise multi-channel implementations on Arm
Cortex-A73 platform using several representative CNNs. The results show
significant performance improvements in full network, up to 60%, over existing
im2row/im2col based optimization techniques

Machine Learning;Emerging Technologies