Abstract
Vectored IR drop analysis is a critical step in chip signoff that checks the power integrity of an on-chip power delivery network. Due to the prohibitive runtimes of dynamic IR drop analysis, the large number of test patterns must be whittled down to a small subset of worst-case IR vectors. Unlike the traditional slow heuristic method that select a few vectors with incomplete coverage, MAVIREC uses machine learning techniques-3D convolutions and regression-like layers-for accurately recommending a larger subset of test patterns that exercise worst-case scenarios. In under 30 minutes, MAVIREC profiles 100K-cycle vectors and provides better coverage than a state-of-the-art industrial flow. Further, MAVIREC's IR drop predictor shows 10X speedup with under 4mV Rmse relative to an industrial flow.
Original language | English (US) |
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Title of host publication | Proceedings of the 2021 Design, Automation and Test in Europe, DATE 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1825-1828 |
Number of pages | 4 |
ISBN (Electronic) | 9783981926354 |
DOIs | |
State | Published - Feb 1 2021 |
Event | 2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021 - Virtual, Online Duration: Feb 1 2021 → Feb 5 2021 |
Publication series
Name | Proceedings -Design, Automation and Test in Europe, DATE |
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Volume | 2021-February |
ISSN (Print) | 1530-1591 |
Conference
Conference | 2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021 |
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City | Virtual, Online |
Period | 2/1/21 → 2/5/21 |
Bibliographical note
Publisher Copyright:© 2021 EDAA.