太赫兹科学与电子信息学报2016，Vol.14Issue(4)：647-652,6.DOI:10.11805/TKYDA201604.0647
一种基于半监督AdaBoost模型树的FPGA性能表征方法
An FPGA performance characterization approach based on semi-supervised AdaBoost model tree
摘要
Abstract
A semi-supervised Adaptive Boosting(AdaBoost) model tree based modeling approach is proposed for Field Programmable Gate Array(FPGA) performance characterization. The proposed approach, which adopts AdaBoost to improve the prediction accuracy, constructs an analytical performance model with regard to the FPGA architecture parameters in semi-supervised learning way. The FPGA performance model built through the proposed approach estimates the area, delay and area-delay product with Mean Relative Errors(MREs) of 4.42%, 1.62% and 5.06%, respectively. Compared to the supervised model tree and the previous semi-supervised model tree algorithm, the proposed approach boosts the estimation accuracy by 39% and 26% respectively. Experimental results show that the proposed approach is proved to be an efficient FPGA characterization approach, building FPGA performance models with high accuracy in less time cost. The proposed modeling approach can be applied to explore the FPGA architecture design space effectively and efficiently.关键词
FPGA性能表征/半监督模型树/AdaBoost模型树Key words
FPGA performance characterization/semi-supervised model tree/AdaBoost model tree分类
信息技术与安全科学引用本文复制引用
杨立群,李威,黄志洪,孙嘉斌,杨海钢..一种基于半监督AdaBoost模型树的FPGA性能表征方法[J].太赫兹科学与电子信息学报,2016,14(4):647-652,6.基金项目
国家自然科学基金资助项目(61271149) （61271149）
