  Recently, efficiently deploying deep learning solutions on the edge has
received increasing attention. New platforms are emerging to support the
increasing demand for flexibility and high performance. In this work, we
explore the efficient mapping of convolutional layers on an open-hardware,
low-power Coarse-Grain Reconfigurable Array (CGRA), namely OpenEdgeCGRA. We
explore both direct implementations of convolution and solutions that transform
it into a matrix multiplication through an Im2col transformation, and
experiment with various tensor parallelism axes. We show that for this hardware
target, direct convolution, coupled with weight parallelism reaches the best
latency and energy efficiency, outperforming a CPU implementation by 3.4x and
9.9x in terms of energy and latency, respectively.
