Title :
High performance relevance vector machine on HMPSoC
Author :
Yongfu He ; Shaojun Wang ; Yu Peng ; Yeyong Pang ; Ning Ma ; Jingyue Pang
Author_Institution :
Harbin Inst. of Technol., Harbin, China
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.
Keywords :
microprocessor chips; multiprocessing systems; pipeline processing; support vector machines; system-on-chip; ARM Cortex A9 processor; AXI-Stream interface; HMPSoC technology; RVM; Xeon 5620 processor; Xilinx Zynq XC7Z020 AP SoC; acceleration engine; energy consumption; hardware codesign approach; intermediate values calculation; kernel formulation; multichannel hardware architecture; on-board PHM prototype platform; pipelined hardware architecture; prognostic and health management; relevance vector machine; software codesign approach; Acceleration; Computational modeling; Engines; Hardware; Prognostics and health management; Random access memory; System-on-chip;
Conference_Titel :
Field-Programmable Technology (FPT), 2014 International Conference on
Print_ISBN :
978-1-4799-6244-0
DOI :
10.1109/FPT.2014.7082812