@inproceedings{956abe6e192746029d9f97797f7268b4,
title = "A Novel Machine-Learning based SoC Performance Monitoring Methodology under Wide-Range PVT Variations with Unknown Critical Paths",
abstract = "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.",
author = "Wang, {Ding Hao} and Lin, {Pei Ju} and Yang, {Hui Ting} and Hsu, {Ching An} and Huang, {Sin Han} and Po-Hung Lin",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 58th ACM/IEEE Design Automation Conference, DAC 2021 ; Conference date: 05-12-2021 Through 09-12-2021",
year = "2021",
month = dec,
day = "5",
doi = "10.1109/DAC18074.2021.9586155",
language = "English",
series = "Proceedings - Design Automation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1370--1371",
booktitle = "2021 58th ACM/IEEE Design Automation Conference, DAC 2021",
address = "United States",
}