research-article

BoA-PTA: A Bayesian Optimization Accelerated PTA Solver for SPICE Simulation

Authors:

Zhou JinAuthors Info & Claims

ACM Transactions on Design Automation of Electronic Systems, Volume 28, Issue 2

Article No.: 27, Pages 1 - 26

https://doi.org/10.1145/3555805

Published: 24 December 2022 Publication History

Abstract

One of the greatest challenges in integrated circuit design is the repeated executions of computationally expensive SPICE simulations, particularly when highly complex chip testing/verification is involved. Recently, pseudo-transient analysis (PTA) has shown to be one of the most promising continuation SPICE solvers. However, the PTA efficiency is highly influenced by the inserted pseudo-parameters. In this work, we proposed BoA-PTA, a Bayesian optimization accelerated PTA that can substantially accelerate simulations and improve convergence performance without introducing extra errors. Furthermore, our method does not require any pre-computation data or offline training. The acceleration framework can either speed up ongoing, repeated simulations (e.g., Monte-Carlo simulations) immediately or improve new simulations of completely different circuits. BoA-PTA is equipped with cutting-edge machine learning techniques, such as deep learning, Gaussian process, Bayesian optimization, non-stationary monotonic transformation, and variational inference via reparameterization. We assess BoA-PTA in 43 benchmark circuits and real industrial circuits against other SOTA methods and demonstrate an average of 1.5x (maximum 3.5x) for the benchmark circuits and up to 250x speedup for the industrial circuit designs over the original CEPTA without sacrificing any accuracy.

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Cited By

Jin ZLi WBai YWang TLu YLiu WKim T(2024)Machine Learning and GPU Accelerated Sparse Linear Solvers for Transistor-Level Circuit Simulation: A Perspective Survey (Invited Paper)Proceedings of the 29th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC58780.2024.10473846(96-101)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1109/ASP-DAC58780.2024.10473846

Index Terms

BoA-PTA: A Bayesian Optimization Accelerated PTA Solver for SPICE Simulation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Kernel methods
        Gaussian processes
2. Hardware
  1. Electronic design automation
    1. Methodologies for EDA
      1. Software tools for EDA

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Published In

cover image ACM Transactions on Design Automation of Electronic Systems

ACM Transactions on Design Automation of Electronic Systems Volume 28, Issue 2

March 2023

409 pages

ISSN:1084-4309

EISSN:1557-7309

DOI:10.1145/3573314

Editor:
X. Sharon Hu
University of Notre Dame, USA

Issue’s Table of Contents

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 24 December 2022

Online AM: 13 August 2022

Accepted: 24 July 2022

Revised: 31 May 2022

Received: 11 March 2022

Published in TODAES Volume 28, Issue 2

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Zhongguancun open laboratory concept verification project
National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province of China
Science Foundation of China University of Petroleum

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Cited By

Jin ZLi WBai YWang TLu YLiu WKim T(2024)Machine Learning and GPU Accelerated Sparse Linear Solvers for Transistor-Level Circuit Simulation: A Perspective Survey (Invited Paper)Proceedings of the 29th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC58780.2024.10473846(96-101)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1109/ASP-DAC58780.2024.10473846

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