Modular design of a factor-graph-based inference engine on a System-On-Chip (SoC)

Abstract

Factor graphs are probabilistic graphical frameworks for modeling complex and dynamic systems. They can be used in a broad range of application domains, from machine learning and robotics, to signal processing and digital communications. One important aspect that makes a factor graph very useful and very promising to be applied widely is its inference mechanism that is suitable for performing a complex model-based reasoning. However, its features have not fully explored and factor graphs are still used mainly as modeling tools that run on standard computers. Whereas in real applications such as robotics, one needs a practical implementation of such a framework. In this paper, we describe the development of a factor-graph-based inference engine that runs on a System-on-Chip (SoC). Running natively on a low level hardware, our factor graph engine delivers highest performance for real-time applications. We designed the embedded architecture so that it conveys important aspects such as modularity, scalability, flexibility and platform-friendly framework. The proposed architecture has customizable levels of parallelism as well as re-configurable modules that are extensible to accommodate large networks. We optimized the design to achieve high efficiency in terms of clock latency and resources consumption. We have tested our design on Xilinx Zynq-7000 SoCs and the implementation result demonstrates that the proposed framework can potentially be extended into a massively distributed probabilistic computing engine.