MAVIREC: ML-Aided Vectored IR-Drop Estimation and Classification
Vidya A. Chhabria1, Yanqing Zhang2, Haoxing Ren2, Ben Keller2, Brucek Khailany2 and Sachin S. Sapatnekar1
1University of Minnesota
2NVIDIA Corporation
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 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.
