Multi-Objective Bayesian Optimization for Analog/RF Circuit Synthesis

dc.contributor.authorZeng, X.
dc.contributor.authorLyu, W.
dc.contributor.authorYang, F.
dc.contributor.authorYan, C.
dc.contributor.authorZhou, Dian
dc.contributor.utdAuthorZhou, Dian
dc.date.accessioned2020-02-25T22:20:14Z
dc.date.available2020-02-25T22:20:14Z
dc.date.created2018-06-24
dc.date.issued2019
dc.descriptionFull text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided Link to Article).en_US
dc.description.abstractIn this paper, a novel multi-objective Bayesian optimization method is proposed for the sizing of analog/RF circuits. The proposed approach follows the framework of Bayesian optimization to balance the exploitation and exploration. Gaussian processes (GP) are used as the online surrogate models for the multiple objective functions. The lower confidence bound (LCB) functions are taken as the acquisition functions to select the data point with best Pareto-dominance and diversity. A modified non-dominated sorting based evolutionary multi-objective algorithm is proposed to find the Pareto Front (PF) of the multiple LCB functions, and the next simulation point is chosen from the PF of the multiple LCB functions. Compared with the multi-objective evolutionary algorithms (MOEA) and the state-of-the-art online surrogate model based circuit optimization method, our method can better approximate the Pareto Front while significantly reduce the number of circuit simulations. © 2018 Association for Computing Machinery.en_US
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.identifier.isbn9781450357005
dc.identifier.urihttps://hdl.handle.net/10735.1/7303
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.urihttp://dx.doi.org/10.1145/3195970.3196078
dc.rights©2018 Association for Computing Machinery
dc.titleMulti-Objective Bayesian Optimization for Analog/RF Circuit Synthesisen_US
dc.typeArticleen_US
dc.type.genrearticle

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