1. [Publications](/publications) 2. MAVIREC: ML-Aided Vectored IR-Drop Estimation and Classification # MAVIREC: ML-Aided Vectored IR-Drop Estimation and Classification ![](/sites/default/files/styles/wide/public/publications/MAVIREC.JPG?itok=S25LvaSN) 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 worstcase 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. ## Authors Vidya A. Chhabria (University of Minnesota) [Yanqing Zhang](/person/yanqing-zhang) Mark Haoxing Ren (NVIDIA) [Ben Keller](/person/ben-keller) [Brucek Khailany](/person/brucek-khailany) Sachin S. Sapatnekar (University of Minnesota) ## Publication Date Monday, February 1, 2021 ## Published in [2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)](https://www.date-conference.com/) ## Research Area [Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence) [Circuits and VLSI Design](/research-area/circuits) ## External Links [MAVIREC: ML-Aided Vectored IR-Drop Estimation and Classification](https://ieeexplore.ieee.org/document/9473914) ## Uploaded Files [DATE2021\_MAVIREC\_Camera\_Ready\_v5.pdf](https://d1qx31qr3h6wln.cloudfront.net/publications/DATE2021_MAVIREC_Camera_Ready_v5.pdf "Open file in new window")751.78 KB ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .