Configurable and scalable belief propagation accelerator for computer vision

Abstract

We demonstrate a novel FPGA-based accelerator architecture that can tackle a range of standard computer vision (CV) problems, with scalable performance and attractive speedups. The architecture relies on multiple pipelined processing elements (PEs) that can be configured to support various belief propagation (BP) settings for different CV tasks. Inside each PE, innovative implementation of Jump Flooding for efficient computation of BP solves the core configurability challenge. A novel block-parallel memory interface supports parallelization by distributing BP inference workloads across the PEs. Experimental results demonstrate that our accelerator achieves scalable performance with 11-41× speedup over standard sequential CPU implementations across a subset of well-known Middlebury and OpenGM benchmarks, with no compromise in quality of inference results. To the best of our knowledge, this is the first FPGA hardware implementation of BP capable of running a range of standard CV benchmarks with significant speedups.