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: Contribution to journalArticlepeer-review

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. The modeling techniques are based on macro-models, which can be assembled to evaluate circuit system performance, and more appealingly can be reused across different circuit topologies. 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)
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
DOIs
StateAccepted/In press - 2024

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Artificial neural networks
  • Circuit optimization
  • Circuits
  • Data models
  • electronic design automation
  • Estimation
  • Integrated circuit modeling
  • machine learning
  • macro modeling
  • Mathematical models
  • Performance modeling

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