Abstract:Massively parallel systolic arrays and resource-efficient depthwise separable convolutions are two promising hardware and software techniques to accelerate DNN inference on the edge. Interestingly, their combination is inefficient: Computational patterns of depthwise separable convolutions do not exhibit a rhythmic systolic flow and lack sufficient data reuse to saturate systolic arrays. In this paper, we formally analyse this inefficiency and propose an efficient operator, an optimal hardware dataflow, and a … Show more
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