Hardware Acceleration of Nonparametric Belief Propagation for Efficient Robot Manipulation

ABSTRACTProbabilistic graphical models (PGMs) have been widely used in computer vision, robotics, and statistics. Generative inference algorithms used to solve PGMs, such as belief propagation (BP), involve integration over high-dimensional variables, which becomes computationally infeasible. Drawing inspiration from particle filters, nonparametric belief propagation (NBP) combines the efficiency of Monte-Carlo sampling to capture the belief space of hidden variables while preserving accuracy and algorithm robustness. In this poster presentation, we describe a novel method to accelerate an NBP algorithm in hardware and evaluate our approach on the 6 degree-of-freedom (DoF) articulated object pose estimation problem. Our two major contributions include 1) identifying three independent computation flows in the algorithm to effectively overlap the two main steps of the algorithm: belief update and message update, and 2) creating deeply pipelined processing units that allow for fine-grained parallelism, which helps to balance the workloads on different computation streams. Results from this study demonstrate that our design achieves both improved runtime and energy efficiency. In particular, we achieved 26X energy saving compared to running the algorithm on a Titan Xp GPU, and 10X runtime speedup and 14X energy saving compared to running on the Jetson AGX . We believe that our FPGA implementation can greatly improve particle-based sampling methods for real time applications.