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  5. Efficiently Exploring FPGA Design Space Based on Semi-Supervised Learning

Efficiently Exploring FPGA Design Space Based on Semi-Supervised Learning

来源 :The 4th International Workshop on Advanced Technologies in P | 被引量 : 0次 | 上传用户：longweii
【摘 要】
：
In this paper,we propose a novel ECOMT predictive modeling approach to present delay and area models of FPGA.Semi-supervised Learning is employed in this approach to automatically generate the model w
【作 者】
：
Liqun Yang Haigang Yang Wei Li Zhihua Li 
【机 构】
：
University of Chinese Academy of Sciences;System on Programmable Chip Research Department,Institute
【出 处】
：
The 4th International Workshop on Advanced Technologies in P
【发表日期】
：
2014年5期
【关键词】
：
FPGA design space exploration predictive modeling Semi-supervised learning ECOMT 
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　　In this paper,we propose a novel ECOMT predictive modeling approach to present delay and area models of FPGA.Semi-supervised Learning is employed in this approach to automatically generate the model with respect to architectural parameters.The process of obtaining model is time-efficient meanwhile the model produced is of high accuracy.According to experimental results,the model generated shows an ability of accurate prediction with MRE(mean relative error)less than 5%.Moreover,the comprehensive model,combined with nonlinear programming,can be further utilized to explore the FPGA design space.The optimum architectural parameters gained will guide FPGA architects to design an optimized FPGA under a specific design specification.
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