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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20260402T024532Z
LOCATION:2012\, Level 2
DTSTART;TZID=America/Los_Angeles:20250623T140000
DTEND;TZID=America/Los_Angeles:20250623T141500
UID:dac_DAC 2025_sess224_ENGPRES079@linklings.com
SUMMARY:Machine Learning based Dynamic IR hotspot estimation for SoC Desig
 ns
DESCRIPTION:Prateek Pendyala, Jingwei Zhang, and T Govindaswamy Rahul Sai 
 (Google)\n\nThis presentation introduces a machine learning (ML) model for
  rapid and accurate dynamic IR drop estimation in SoC designs. Traditional
  dynamic IR estimation methods are computationally expensive, with runtime
  complexity of N², hindering timely design finalization with good PPA metr
 ics.\nThis work proposes an XGBoost regression-based ML model to predict v
 ector-less dynamic IR using power and timing features.\nTested on two indu
 strial SoCs in most recent process nodes, with over 1.5 million instances,
  the ML model achieved a 15x speedup.The model maintained accuracy with le
 ss than 1 mV Mean Square Error and a correlation coefficient of ~0.85. ROC
  accuracy of ~90.0 indicates close approximation of predicted vs. actual I
 R drop.\n\nTopics: AI, Back-End Design, Chiplet\n\nSession Chair: Badhri U
 ppiliappan (BAE Systems)\n\n
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