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Title: MMM: Machine Learning-Based Macro-Modeling for Linear Analog ICs and ADC/DACs
Performance modeling is a key bottleneck for analog design automation. Although machine learning-based models have advanced the state-of-the-art, they have so far suffered from huge data preparation cost, very limited reusability, and inadequate accuracy for large circuits. We introduce ML-based macro-modeling techniques to mitigate these problems for linear analog ICs and ADC/DACs. On representative testcases, our method achieves more than 1700× speedup for data preparation and remarkably smaller model errors compared to recent ML approaches. It also attains 3600× acceleration over SPICE simulation with very small errors and reduces data preparation time for an ADC design from 40 days to 9.6 hours.  more » « less
Award ID(s):
2212345
PAR ID:
10464361
Author(s) / Creator(s):
Date Published:
Journal Name:
Proceedings of the ACM/IEEE Workshop on Machine Learning for CAD
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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