Resource-efficient Reconfigurable Computer-on-Module for Embedded Vision Applications

Abstract

The paper proposes a novel architecture for a highly customisable FPGA-SoC-based Computer-on-Module (CoM) targeting embedded vision applications. Apart from a Xilinx Zynq SoC, the module integrates an Adapteva Epiphany floating point accelerator in a Toradex Apalis compliant form factor. The CoM has been successfully integrated into two robot platforms to enhance their vision processing capabilities. For evaluation, visually-guided collision avoidance and navigation has been implemented, mimicking the behaviour of insects. The hardware/software partitioning is presented together with a comparison to an HLS-based solution for the given application. The proposed stream-based FPGA implementation achieves a speedup of 721 and an increase in energy efficiency by a factor of 800 compared to an OpenCV-based implementation on one of the embedded ARM processors of the Zynq SoC.