Efficient Co-Approximate Parallel Compressive Depth Reconstruction on FPGA

Abstract

Efficient depth image reconstruction from sparse samples is crucial for machine perception applications, such as robotics, vehicle assistance and autonomy. It demands fast processing speed with low power consumption for sensing quality and safety, as well as cost reduction for FPGA and solid state implementations, within constrained resource budgets on edge devices. A new co-approximate framework of parallel approximate compressive depth reconstruction engine on FPGA is proposed using ℓ<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> solvers, proximal gradient decent (PGD), with instrumented frequency and voltage scaling during the iterative optimization process. By evaluating various number of parallel approximate processing units for the depth image reconstruction engine, up to 51% further power saving is achieved, and 421× speed up of parallel processing compared to the baseline, henceforth the efficiency is elevated over 43×.