Energy-Efficient Acceleration of OpenCV Saliency Computation Using Soft Vector Processors

Abstract

Soft vector processors in embedded FPGA platforms such as the Vector Blox MXP engine can match the performance and exceed the energy-efficiency of commercial off-the-shelf embedded SoCs with SIMD or GPU accelerators for OpenCV applications such as Saliency detection. We are also able to beat spatial hardware designs built from high-level synthesis while requiring significantly lower programming effort. These improvements are possible through careful scheduling of DMA operations to the vector engine, extensive use of line-buffering to enhance data reuse on the FPGA and limited use of scalar fallback for non-vectorizable code. The driving principle is to keep data and computation on the FPGA for as long as possible to exploit parallelism, data locality and lower the energy requirements of communication. Using our approach, we outperform all platforms in our architecture comparison while needing less energy. At640×480 image resolution, our implementation of MXP soft vector processor on the Xilinx Zed board exceeds the performance of the Jetson TK1-GPU by 1.5× while needing 1.6× less energy, Beagle bone Black by 4.7× at 2.3× less energy, Raspberry Piby 9× at 4× less energy, and Intel Galileo by 28× at 16× less energy. Our vector implementation also outperforms Vivado HLS generated OpenCV library implementation by 1.5×.