FPGA implementation of Bayesian network inference for an embedded diagnosis

Sara Zermani, Catherine Dezan, Hanen Chenini, Jean-Philippe Diguet, Reinhardt Euler

2015 · 33 citations · 22 references

Concepts

Abstract

Critical systems, like Unmanned Aerial Systems (UAS) operate in uncertain environments and have to face unexpected obstacles, weather changes and sensor, hardware or software failures. Therefore, a health management system is needed to detect and locate the failure in real time. In this paper, we propose a Field Programmable Gate Array (FPGA) implementation based on a Bayesian network (BN) representation, that allows to continuously monitor the embedded system under time and resource constraints. The hardware implementation is generated by a specific off-line framework integrating a high-level synthesis tool. The proposal is evaluated on a hybrid reconfigurable device to show potential speed-up. Some variations on the hardware implementation are also explored to give the best trade-off between accuracy, performance and resource allocation.

References

22