Patra: Parallel tree-reweighted message passing architecture

Maximum a posteriori probability inference algorithms for Markov Random Field are widely used in many applications, such as computer vision and machine learning. Sequential tree-reweighted message passing (TRW-S) is an inference algorithm which shows good quality in finding optimal solutions. However, the performance of TRW-S in software cannot meet the requirements of many real-time applications, due to the sequential scheme and the high memory, bandwidth and computational costs. This paper proposes Patra, a novel parallel tree-reweighted message passing architecture, which involves a fully pipelined design targeting FPGA technology. We build a hybrid CPU/FPGA system to test the performance of Patra for stereo matching. Experimental results show that Patra provides about 100 times faster than a software implementation of TRW-S, and 12 times faster than a GPU-based message passing algorithm. Compared with an existing design in four FPGAs, we can achieve 2 times speedup in a single FPGA. Moreover, Patra can work at video rate in many cases, such as a rate of 167 frame/sec for a standard stereo matching test case, which makes it promising for many real-time applications.