Abstract

A factor graph (FG) can be considered as a unified model combining a Bayesian network (BN) and a Markov random field (MRF). The inference mechanism of a FG can be used to perform reasoning under incompleteness and uncertainty, which is a challenging task in many intelligent systems and robotics. Unfortunately, a complete inference mechanism requires intense computations that introduces a long delay for the reasoning process to complete. Furthermore, in an energy-constrained system such as a mobile robot, it is required to have a very efficient inference process. In this paper, we present an embedded FG inference engine that employs a neural-inspired discretization mechanism. The engine runs on a system-on-chip (SoC) and is accelerated by its FPGA. We optimized our design to balance the trade-off between speed and hardware resource utilization. In our fully-optimized design, it can accelerate the inference process eight times faster than the normal execution, which is twice the speed-up gain achieved by a parallelized FG running on a PC. The experiments demonstrate that our design can be extended into an efficient reconfigurable computing machine.