一种在线时间序列预测的核自适应滤波器向量处理器

doi:10.11999/JEIT150157

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Abstract

To address the online time series prediction problem in CPS (Cyber-Physical System) system, both KAF (Kernel Adaptive Filter) with low computation complexity and adaptive characteristic and FPGA computing system are employed. A novel FPGA implementation of vector processor targeting KAF algorithm is proposed. The parallelized datapath and multi-stage pipeline are introduced to enhance the performance and reduce the power consumption and latency. The microcoding technology is further employed to improve the reusability and extensibility. The classical KAF algorithms are implemented based on the vector processor. Experiments results show that the proposed vector processor improves the execution speed by factors of 22, the power consumption decrease to 1/139, while the latency decrease to 1/26 compared with a CPU, on the condition that the precision meets the requirement.

Key words： Kernel Adaptive Filter (KAF) Filed-Programmable Gate Arrays (FPGA) Vector processor Microcode

Received: 27 January 2015 Published: 17 November 2015

PACS:

TP391

Fund:

The National Natural Science Foundation of China (61571160/F011305), Fundamental Research Funds for the Central Universities (HIT.NSRIF.201615)

Corresponding Authors: PANG Yeyong E-mail: yeyongpang@126.com

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	PANG Yeyong
	WANG Shaojun
	PENG Yu
	PENG Xiyuan

Cite this article:

PANG Yeyong,WANG Shaojun,PENG Yu等. A Kernel Adaptive Filter Vector Processor for Online Time Series Prediction[J]. JEIT, 2016, 38(1): 53-62.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT150157 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I1/53

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