A Kernel Adaptive Filter Vector Processor for Online Time Series Prediction

PANG Yeyong; WANG Shaojun; PENG Yu; PENG Xiyuan

doi:10.11999/JEIT150157

Volume 38 Issue 1

Jan. 2016

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PANG Yeyong, WANG Shaojun, PENG Yu, PENG Xiyuan. A Kernel Adaptive Filter Vector Processor for Online Time Series Prediction[J]. Journal of Electronics & Information Technology, 2016, 38(1): 53-62. doi: 10.11999/JEIT150157

Citation:

PANG Yeyong, WANG Shaojun, PENG Yu, PENG Xiyuan. A Kernel Adaptive Filter Vector Processor for Online Time Series Prediction[J]. Journal of Electronics & Information Technology, 2016, 38(1): 53-62. doi: 10.11999/JEIT150157

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A Kernel Adaptive Filter Vector Processor for Online Time Series Prediction

doi: 10.11999/JEIT150157

Funds:

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

Received Date: 2015-01-27
Rev Recd Date: 2015-09-28
Publish Date: 2016-01-19

Abstract

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.
- Kernel Adaptive Filter (KAF),
- Filed-Programmable Gate Arrays (FPGA),
- Vector processor,
- Microcode

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References(27)

References

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