research-article

Transfer learning with bayesian optimization-aided sampling for efficient AMS circuit modeling

Authors:

Mohsen Hassanpourghadi,

Mike Shuo-Wei ChenAuthors Info & Claims

ICCAD '20: Proceedings of the 39th International Conference on Computer-Aided Design

Article No.: 119, Pages 1 - 9

https://doi.org/10.1145/3400302.3415687

Published: 17 December 2020 Publication History

Abstract

A traditional analog mixed-signal (AMS) design mostly relies on the designer's knowledge and can only afford exploring over a narrow design space due to expensive SPICE simulation. However, a neural network (NN)-based model of an AMS circuit potentially enables fast exploration of the design space thanks to its low computation cost. Unfortunately, to build an NN model with sufficient accuracy, a training dataset is needed, incurring SPICE simulations during different design phases. Therefore, it is prudent to train it with a larger dataset in an earlier design phase (e.g. schematic design) but a significantly reduced dataset in a later design phase (e.g. postlayout design or migration to more advanced technology node), as simulation cost increases sharply in later design phases. In this paper, we propose the use of transfer learning (TL) with Bayesian optimization-aided sampling (BOAS) to reduce the required size of training datasets for NN models in later design phases. To prove the concept, we show that 150X and 17X dataset reductions are possible for a digital-to-analog converter (DAC) in the post-layout design phase and an amplifier in the technology migration phase, respectively.

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cover image ACM Conferences

ICCAD '20: Proceedings of the 39th International Conference on Computer-Aided Design

November 2020

1396 pages

ISBN:9781450380263

DOI:10.1145/3400302

General Chair:
Yuan Xie
Univ. of California, Santa Barbara, CA

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 December 2020

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ICCAD '20: IEEE/ACM International Conference on Computer-Aided Design

November 2 - 5, 2020

Virtual Event, USA

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https://doi.org/10.1109/TCAD.2024.3377162
Gao XZhang HYe SLiu MPan DShen LWang RLin YHuang R(2024)Post-layout simulation driven analog circuit sizingScience China Information Sciences10.1007/s11432-022-3878-567:4Online publication date: 20-Mar-2024
https://doi.org/10.1007/s11432-022-3878-5
Huang JWang CYan YTao CYang FYan CHu WZhou DZeng X(2023)An Analog Circuit Building Block Generator via Nested Multi-Fidelity ModelingIEEE Transactions on Circuits and Systems I: Regular Papers10.1109/TCSI.2023.327359370:8(3280-3293)Online publication date: Aug-2023
https://doi.org/10.1109/TCSI.2023.3273593
Chai ZZhao YLiu WLin YWang RHuang R(2023)CircuitNet: An Open-Source Dataset for Machine Learning in VLSI CAD Applications With Improved Domain-Specific Evaluation Metric and Learning StrategiesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.328797042:12(5034-5047)Online publication date: Dec-2023
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Zhao ZLuo JLiu JZhang L(2023)Signal-Division-Aware Analog Circuit Topology Synthesis Aided by Transfer LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.324597942:11(3481-3490)Online publication date: Nov-2023
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Lin YLi YMadhusudan MSapatnekar SHarjani RHu J(2023)MMM: Machine Learning-Based Macro-Modeling for Linear Analog ICs and ADC/DACs2023 ACM/IEEE 5th Workshop on Machine Learning for CAD (MLCAD)10.1109/MLCAD58807.2023.10299829(1-6)Online publication date: 10-Sep-2023
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