LoCoMOBO: A Local Constrained Multiobjective Bayesian Optimization for Analog Circuit Sizing

Touloupas, Konstantinos; Sotiriadis, Paul P.

doi:10.1109/tcad.2021.3121263

Cited by 19 publications

(9 citation statements)

References 38 publications

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“…The common ground between GA MOO and TS efficient optimization is that both algorithms begin by randomly initializing some set of data points (Touloupas and Sotiriadis, 2021). However, the GA produces offsprings from parents through the cross-over operation and mutation, while in the TS algorithm, new data points are generated using sample functions from the Gaussian processes.…”

Section: Discussionmentioning

confidence: 99%

A socio-economic multi-objective optimization approach toward enhancing public–private partnerships’ concession agreements in infrastructure development

Alghamdi

2022

Journal of Strategic Contracting and Negotiation

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The participants of the public–private partnership projects have different parts in developing the concessions, hence different perspectives and goals. Besides, the public–private partnership concession agreement has many parameters and components, and a change in one component will have a considerable impact on other components. Thus, determining the optimal value for different concession parameters by providing a series of feasible contribution options was investigated in this paper using two different multi-objective optimization models: genetic algorithm and Thompson sampling. Two case studies (the US I-495 and the I-4 Ultimate) were used to construct and validate the results of the model. The model results showed that the socio-economic sustainability performance increases as the private equity increases and the public equity decreases. The results also showed that the socio-economic sustainability performance increases as the concession price (user-fee) decreases. Having these contribution options could facilitate the decision-making process for both the public and private parties. The genetic algorithm model obtained faster optimization results when compared to the Thompson sampling model; however, the Thompson sampling obtained better results. Moreover, the use of the model could improve the socio-economic sustainability performance of the public–private partnership projects.

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Section: Discussionmentioning

confidence: 99%

A socio-economic multi-objective optimization approach toward enhancing public–private partnerships’ concession agreements in infrastructure development

Alghamdi

2022

Journal of Strategic Contracting and Negotiation

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“…Modern EDA tools for HLS, logic synthesis, or physical design synthesis have many parameters, GPR + WEI Amp-1,2 (C-5,C-24), etc., First few apply BO for analog sizing [73] GPR + LCB Amp-1,2 (C-5,C-24), etc., NSGAII solves multi-objective LCB [74] GPR + Acq ensemble Amp-1,2 (C-10,C-12) Batch BO enabled by the ensemble [75] GPR + WEI Op-Amp (C-10), CP (C-36) Neural network as GPR kernel [77] BNN + LCB CP (C-16), Amp (C-13) BNN as surrogate model [76] GPR + WEI Amp (C-5), CP (C-36) Multi-fidelity BO [78] GPC + its Eq. ( 45) Op-Amp(C-4), LNA (C-4) Handles binary testing outputs [82] GPR + WEI CP (C-18, D-5), etc., Modified kernel for D variables [79] GPR + UCB Ampifier-1,2 (C-10,C-12) Asynchronous BO [80] SP-GPR + WEI Amp (C-24), VCO (C-20) Sparse GPR model [81] GPR + EI Op-Amp (variable unknown) Classical BO [88] MT-GPR + WEI Op-Amp (C-10), LNA (C-10) MT-GPR learns multi-performances [83] GPR + TS Amp-1,2,3 (C-23, C-43, C-21)…”

Section: B Other Problems and Discussionmentioning

confidence: 99%

“…( 34) as a new function f (x) with a slight abuse of notation. This treatment is frequently used in analog circuit optimization over multiple process-voltage-temperature (PVT) corners [83]. Namely, in these works, n represents the equivalent variation introduced by the n-th PVT corner and evaluating f (x + n ) will invoke the circuit simulator at the n-th PVT corner.…”

Section: A Schematic-level Circuit Optimizationmentioning

confidence: 99%

Automatic Design of a Broadband Directional Coupler via Bayesian Optimization

Gao

Zhang

Boning

2022

Conference on Lasers and Electro-Optics

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“…Efforts to establish means of automation in the analog design cycle include mostly sizing automation approaches [5][6][7][8][9][10]. In this setting, the sizing procedure of a given analog circuit topology is formulated as a black-box optimization problem, where the design space includes all the unknown design variables and the optimization goals and constraints rely on the desired performance metrics.…”

Section: Introductionmentioning

confidence: 99%

“…A low simulation budget alternative is the Bayesian optimization (BO) algorithm [12], which derives good approximations of the optima with relatively fewer evaluations than EAs [12]. BO has been applied to analog circuit sizing, both in the multi-objective [5] and in the single-objective settings [8].…”

Section: Introductionmentioning

confidence: 99%

Mixed-Variable Bayesian Optimization for Analog Circuit Sizing through Device Representation Learning

Touloupas

Sotiriadis

2022

Electronics

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In this work, a deep representation learning method is proposed to build continuous-valued representations of individual integrated circuit (IC) devices. These representations are used to render mixed-variable analog circuit sizing problems as continuous ones and to apply a low-budget black box Bayesian optimization (BO) variant to solve them. By transforming the initial search spaces into continuous-valued ones, the BO’s Gaussian process models (GPs), which typically operate on real-valued spaces, can be used to guide the optimization search towards the global optimum. The proposed Device Representation Learning approach involves using device simulation data and training a composite model of a Variational Autoencoder (VAE) and a dense Neural Network. The latent variables of the trained VAE model serve as the representations of the integrated device and replace the discrete-valued parametrizations of particular devices. A thorough explanation of the proposed methodology’s mathematical formulation is given and example sizing applications on real-world analog circuits and integrated devices underline its efficiency.

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LoCoMOBO: A Local Constrained Multiobjective Bayesian Optimization for Analog Circuit Sizing

Cited by 19 publications

References 38 publications

A socio-economic multi-objective optimization approach toward enhancing public–private partnerships’ concession agreements in infrastructure development

A socio-economic multi-objective optimization approach toward enhancing public–private partnerships’ concession agreements in infrastructure development

Automatic Design of a Broadband Directional Coupler via Bayesian Optimization

Mixed-Variable Bayesian Optimization for Analog Circuit Sizing through Device Representation Learning

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