Convolution neural network is widely used in various fields. The convolution layer is the core layer of the convolution neural network. The back propagation speed of the convolution layer will directly affect the training speed of the whole network, thus affecting the whole performance. For the convolution layer with stride ≥ 2, the error transmission phase of back propagation will carry out a large amount of padding in the feature graph, resulting in a large amount of additional overhead in access and calculation. In this case, we propose a new optimization method, which can reduce the overhead caused by padding to almost zero, and implement it by implicit convolution on domestic heterogeneous platforms. The experiment shows that the performance of the operator optimized by this method is nearly 50% higher than that of the original operator of the platform, and the average performance reaches 90% of that of NVIDIA V100 operator.
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