  Depthwise convolutions provide significant performance benefits owing to the
reduction in both parameters and mult-adds. However, training depthwise
convolution layers with GPUs is slow in current deep learning frameworks
because their implementations cannot fully utilize the GPU capacity. To address
this problem, in this paper we present an efficient method (called diagonalwise
refactorization) for accelerating the training of depthwise convolution layers.
Our key idea is to rearrange the weight vectors of a depthwise convolution into
a large diagonal weight matrix so as to convert the depthwise convolution into
one single standard convolution, which is well supported by the cuDNN library
that is highly-optimized for GPU computations. We have implemented our training
method in five popular deep learning frameworks. Evaluation results show that
our proposed method gains $15.4\times$ training speedup on Darknet, $8.4\times$
on Caffe, $5.4\times$ on PyTorch, $3.5\times$ on MXNet, and $1.4\times$ on
TensorFlow, compared to their original implementations of depthwise
convolutions.

Computer Vision and Pattern Recognition