High performance relevance vector machine on HMPSoC

Abstract

Relevance Vector Machine (RVM) with the uncertainty expressing ability has spawned broad applications in Prognostic and Health Management (PHM). However computationally intensive intrinsic nature of RVM greatly limits its usage. This paper presents a software and hardware co-design approach based on HMPSoC technology, which efficiently exploited sequential and parallel nature of RVM. Multi-channel and pipelined hardware architecture for the acceleration of kernel formulation and intermediate values calculation is proposed. The hardware that wrapped with AXI-Stream interface is integrated into HMPSoC as an acceleration engine. We implement the design on an on-board PHM prototype platform with a Xilinx Zynq XC7Z020 AP SoC. The experiment results show 5.3× and 46.8× speed up in terms of the time cost than the RVM running on PC with a Xeon 5620 processor and ARM Cortex A9 processor. The energy consumption is reduced by 153.0× and 37.3×, respectively.