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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20211208T164413Z
LOCATION:Level 2 - Lobby
DTSTART;TZID=America/Los_Angeles:20211208T180000
DTEND;TZID=America/Los_Angeles:20211208T190000
UID:dac_DAC 2021_sess243_WIP151@linklings.com
SUMMARY:GridNetOpt: Fast Full-Chip EM-Aware IR Drop Constrained Power Grid
  Optimization via Deep Neural Networks
DESCRIPTION:Networking Reception, Work-in-Progress Poster\n\nGridNetOpt: F
 ast Full-Chip EM-Aware IR Drop Constrained Power Grid Optimization via Dee
 p Neural Networks\n\nLiu\n\nThis paper presents a fast full-chip EM-aware 
 IR drop constrained optimization framework, named GridNetOpt, for on-chip 
 power grid networks via a data-driven and learning-based IR drop modeling.
  The new method can naturally take advantage of differential characteristi
 cs of DNN for fast sensitivity computation. First, a CGAN model is trained
  where the training data comes from a state-of-the-art EM-IR analysis tool
 . Second, conjugate gradient based optimization framework is employed. Com
 puting from the CGAN model, sensitivity information is obtained trivially 
 through its auto-differentiation process. Numerical results show that the 
 proposed GridNetOpt leads to order of magnitude speedup over the adjoint n
 etwork method.\n\nVirtual Program: Presented In-Person
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