Paper

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

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... (read more)

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