FPGA implementation of Bayesian network inference for an embedded diagnosis

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.