一種在線時(shí)間序列預(yù)測(cè)的核自適應(yīng)濾波器向量處理器
doi: 10.11999/JEIT150157
基金項(xiàng)目:
國(guó)家自然科學(xué)基金(61571160/F011305)，中央高?；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金資助(HIT.NSRIF.201615)
A Kernel Adaptive Filter Vector Processor for Online Time Series Prediction
Funds:
The National Natural Science Foundation of China (61571160/F011305), Fundamental Research Funds for the Central Universities (HIT.NSRIF.201615)
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摘要: 針對(duì)信息物理融合系統(tǒng)中的在線時(shí)間序列預(yù)測(cè)問(wèn)題，該文選擇計(jì)算復(fù)雜度低且具有自適應(yīng)特點(diǎn)的核自適應(yīng)濾波器(Kernel Adaptive Filter, KAF)方法與FPGA計(jì)算系統(tǒng)相結(jié)合，提出一種基于FPGA的KAF向量處理器解決思路。通過(guò)多路并行、多級(jí)流水線技術(shù)提高了處理器的計(jì)算速度，降低了功耗和計(jì)算延遲，并采用微碼編程提高了設(shè)計(jì)的通用性和可擴(kuò)展性。該文基于該向量處理器實(shí)現(xiàn)了經(jīng)典的KAF方法，實(shí)驗(yàn)表明，在滿足計(jì)算精度要求的前提下，該向量處理器與CPU相比，最高可獲得22倍計(jì)算速度提升，功耗降為1/139，計(jì)算延遲降為1/26。
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關(guān)鍵詞:
- 核自適應(yīng)濾波器(KAF) /
- 現(xiàn)場(chǎng)可編程邏輯門(mén)陣列(FPGA) /
- 向量處理器 /
- 微碼
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. -
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