TOCO: A Systolic Network for Efficient Transposed Convolutions with Output-Reuse Paths

Transposed convolution is a learnable up-sampling operator widely-used in deep neural networks. It up-samples the input activations to generate useful information in applications like style transfer and super resolution. There exists a rising demand for accelerating transposed convolution layers since they occupy a large portion of computation in GAN-like networks.