FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning

Design flow parameters are of utmost importance to chip design quality and require a painfully long time to evaluate their effects. In reality, flow parameter tuning is usually performed manually based on designers' experience in an ad hoc manner. In this work, we introduce a machine learning-b...

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Veröffentlicht in:	Proceedings of the ASP-DAC ... Asia and South Pacific Design Automation Conference S. 19 - 25
Hauptverfasser:	Xie, Zhiyao, Fang, Guan-Qi, Huang, Yu-Hung, Ren, Haoxing, Zhang, Yanqing, Khailany, Brucek, Fang, Shao-Yun, Hu, Jiang, Chen, Yiran, Barboza, Erick Carvajal
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.01.2020
Schlagworte:	Integrated circuit modeling Kernel Random forests Semisupervised learning Task analysis Tuning Vegetation
ISSN:	2153-697X
Online-Zugang:	Volltext
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