MMM: Machine Learning-Based Macro-Modeling for Linear Analog ICs and ADC/DACs

Yishuang Lin, Yaguang Li, Meghna Madhusudan, Sachin S. Sapatnekar, Ramesh Harjani, Jiang Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2023 ACM/IEEE 5th Workshop on Machine Learning for CAD, MLCAD 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350309553
DOIs
StatePublished - 2023
Event5th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2023 - Snowbird, United States
Duration: Sep 10 2023Sep 13 2023

Publication series

Name2023 ACM/IEEE 5th Workshop on Machine Learning for CAD, MLCAD 2023

Conference

Conference5th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2023
Country/TerritoryUnited States
CitySnowbird
Period9/10/239/13/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

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