A Novel Machine-Learning based SoC Performance Monitoring Methodology under Wide-Range PVT Variations with Unknown Critical Paths

Ding Hao Wang, Pei Ju Lin, Hui Ting Yang, Ching An Hsu, Sin Han Huang, Po-Hung Lin

研究成果: Conference contribution同行評審

摘要

Monitoring system-on-chip performance under process, voltage, and temperature (PVT) variations is very challenging, especially when the parasitic effects dominate the whole chip performance in advanced process nodes. Most of the previous works presented the performance monitoring methodologies based on known/predicted candidates of critical paths under different operating conditions. However, those methodologies may fail when the critical path is misrecognized or mispredicted. This paper proposes a novel machine-learning based chip performance monitoring methodology to accurately match the chip performance without requiring the information of critical paths under various PVT conditions. The experimental results based on measured chip performance show that the proposed methodology can achieve 98.5% accuracy in the worst case under wide-range PVT variations.

原文English
主出版物標題2021 58th ACM/IEEE Design Automation Conference, DAC 2021
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1370-1371
頁數2
ISBN(電子)9781665432740
DOIs
出版狀態Published - 5 12月 2021
事件58th ACM/IEEE Design Automation Conference, DAC 2021 - San Francisco, United States
持續時間: 5 12月 20219 12月 2021

出版系列

名字Proceedings - Design Automation Conference
2021-December
ISSN(列印)0738-100X

Conference

Conference58th ACM/IEEE Design Automation Conference, DAC 2021
國家/地區United States
城市San Francisco
期間5/12/219/12/21

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