Tiered Acquisition for Constrained Bayesian Optimization: An Application to Analog Circuits

• URL: http://arxiv.org/abs/2412.17360v1
• Date: Mon, 23 Dec 2024 07:41:43 GMT
• Title: Tiered Acquisition for Constrained Bayesian Optimization: An Application to Analog Circuits
• Authors: Ria Rashid, Abhishek Gupta,
• Abstract summary: We propose a novel Bayesian optimization algorithm with a tiered ensemble of acquisition functions.<n>The method is validated in gain and area optimization of a two-stage Miller compensated operational amplifier in a 65 nm technology.<n>In comparison to robust baselines and state-of-the-art algorithms, this method reduces constraint violations by up to 38% and improves the target objective by up to 43%.
• Score: 5.353688923855625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Analog circuit design can be considered as an optimization problem with the targeted circuit specifications as constraints. When stringent circuit specifications are considered, it is desired to have an optimization methodology that adapts well to heavily constrained search spaces. To this end, we propose a novel Bayesian optimization algorithm with a tiered ensemble of acquisition functions and demonstrate its considerable application potential for analog circuit design automation. Our method is the first to introduce the concept of multiple dominance among acquisition functions, allowing the search for the optimal solutions to be effectively bounded \emph{within} the predicted set of feasible solutions in a constrained search space. This has resulted in a significant reduction in constraint violations by the candidate solutions, leading to better-optimized designs within tight computational budgets. The methodology is validated in gain and area optimization of a two-stage Miller compensated operational amplifier in a 65 nm technology. In comparison to robust baselines and state-of-the-art algorithms, this method reduces constraint violations by up to 38% and improves the target objective by up to 43%. The source code of our algorithm is made available at https://github.com/riarashid/TRACE.

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