Boosting VLSI Design Flow Parameter Tuning with Random Embedding and Multi-objective Trust-region Bayesian Optimization

Su Zheng, Hao Geng, Chen Bai, Bei Yu*, Martin D F Wong

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


Modern very large-scale integration (VLSI) design requires the implementation of integrated circuits using electronic design automation (EDA) tools. Due to the complexity of EDA algorithms, there are numerous tool parameters that have imperative impacts on the chip design quality. Manual selection of parameter values is excessively laborious and constrained by experts’ experience. Due to the high complexity and lack of parallelization, most existing parameter tuning methods cannot make sufficient exploration in a large search space. In this article, we boost the efficiency and performance of parameter tuning with random embedding and multi-objective trust-region Bayesian optimization. Random embedding can effectively cut down the number of variables in the search process and thus reduce the runtime of Bayesian optimization. Multi-objective trust-region Bayesian optimization allows the algorithm to explore diverse solutions with excellent parallelism. Due to the ability to do more exploration in limited runtime, the proposed framework can achieve better performance than existing methods in our experiments.
Original languageEnglish
Article number74
Number of pages23
JournalACM Transactions on Design Automation of Electronic Systems
Issue number5
Publication statusPublished - 9 Sept 2023

Scopus Subject Areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

  • Bayesian optimization
  • Parameter tuning
  • Physical design
  • VLSI design flow
  • parameter tuning


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