PTPT: Physical Design Tool Parameter Tuning via Multi-Objective Bayesian Optimization

Physical design flow through associated electronic design automation (EDA) tools plays an imperative role in the advanced integrated circuit design. Mostly, the parameters fed into physical design tools are mainly manually picked based on the domain knowledge of the experts. Nevertheless, owing to t...

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Vydáno v:IEEE transactions on computer-aided design of integrated circuits and systems Ročník 42; číslo 1; s. 178 - 189
Hlavní autoři: Geng, Hao, Chen, Tinghuan, Ma, Yuzhe, Zhu, Binwu, Yu, Bei
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Bayes methods
Bayesian analysis
Circuit design
Complexity
Configuration management
Delays
Design optimization
Design parameters
Electronic design automation
Integrated circuits
Mathematical models
Measurement
Multi-objective Bayesian optimization (BO)
multi-task Gaussian process
Multiple objective analysis
Multitasking
Optimization
Physical design
tool parameter tuning
Tuning
ISSN:0278-0070, 1937-4151
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Shrnutí:Physical design flow through associated electronic design automation (EDA) tools plays an imperative role in the advanced integrated circuit design. Mostly, the parameters fed into physical design tools are mainly manually picked based on the domain knowledge of the experts. Nevertheless, owing to the ever-shrinking scaling down of technology nodes and the complexity of the design space spanned by combinations of the parameters, even coupled with the time-consuming simulation process, such manual explorations for parameter configurations of physical design tools have become extremely laborious. There exist a few works in the field of design flow parameter tuning. However, very limited prior arts explore the complex correlations among multiple quality-of-result (QoR) metrics of interest (e.g., delay, power, and area) and explicitly optimize these goals simultaneously. To overcome these weaknesses and seek effective parameter settings of physical design tools, in this article, we propose a multi-objective Bayesian optimization (BO) framework with a multi-task Gaussian model as the surrogate model. An information gain-based acquisition function is adopted to sequentially choose candidates for tool simulation to efficiently approximate the Pareto-optimal parameter configurations. The experimental results on three industrial benchmarks under the 7-nm technology node demonstrate the superiority of the proposed framework compared to the cutting-edge works.
Bibliografie:ObjectType-Article-1
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ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2022.3167858