dMazeRunner: Optimizing Convolutions on Dataflow Accelerators

Abstract

Convolution neural networks (CNNs) can be efficiently executed on dataflow accelerators. However, the vast space of executing convolutions on computational and memory resources of accelerators makes difficult for programmers to automatically and efficiently accelerate the convolutions and for architects to achieve efficient accelerator designs. We propose dMazeRunner framework, which allows users to optimize execution methods for accelerating convolution and matrix multiplication on a given architecture and to explore dataflow accelerator designs for efficiently executing CNN models. dMazeRunner determines efficient dataflows tailored for CNN layers and achieves efficient execution methods for CNN models within several seconds.