An Efficient Multi-fidelity Bayesian Optimization Approach for Analog Circuit Synthesis

This paper presents an efficient multi-fidelity Bayesian optimization approach for analog circuit synthesis. The proposed method can significantly reduce the overall computational cost by fusing the simple but potentially inaccurate low-fidelity model and a few accurate but expensive high-fidelity data. Gaussian Process (GP) models are employed to model the low-and high-fidelity black-box functions separately. The nonlinear map between the low-fidelity model and high-fidelity model is also modelled as a Gaussian process. A fusing GP model which combines the low-and high-fidelity models can thus be built. An acquisition function based on the fusing GP model is used to balance the exploitation and exploration. The fusing GP model is evolved gradually as new data points are selected sequentially by maximizing the acquisition function. Experimental results show that our proposed method reduces up to 65.5% of the simulation time compared with the state-of-the-art single-fidelity Bayesian optimization method, while exhibiting more stable performance and a more promising practical prospect.

[1]  Donald R. Jones,et al.  Global versus local search in constrained optimization of computer models , 1998 .

[2]  Stephen P. Boyd,et al.  Optimization of phase-locked loop circuits via geometric programming , 2003, Proceedings of the IEEE 2003 Custom Integrated Circuits Conference, 2003..

[3]  J. Dennis,et al.  MANAGING APPROXIMATION MODELS IN OPTIMIZATION , 2007 .

[4]  Matt J. Kusner,et al.  Bayesian Optimization with Inequality Constraints , 2014, ICML.

[5]  Rob A. Rutenbar,et al.  Hierarchical Modeling, Optimization, and Synthesis for System-Level Analog and RF Designs , 2007, Proceedings of the IEEE.

[6]  Xuan Zeng,et al.  Smart-MSP: A Self-Adaptive Multiple Starting Point Optimization Approach for Analog Circuit Synthesis , 2018, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[7]  A. O'Hagan,et al.  Predicting the output from a complex computer code when fast approximations are available , 2000 .

[8]  Revna Acar Vural,et al.  International Journal of Electronics and Communications (aeü) Analog Circuit Sizing via Swarm Intelligence , 2022 .

[9]  Nando de Freitas,et al.  Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.

[10]  Andreas C. Damianou,et al.  Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling , 2017, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[11]  Stephen P. Boyd,et al.  A tutorial on geometric programming , 2007, Optimization and Engineering.

[12]  LiuBo,et al.  Analog circuit optimization system based on hybrid evolutionary algorithms , 2009 .

[13]  Zheng Wang,et al.  Analog circuit optimization system based on hybrid evolutionary algorithms , 2009, Integr..

[14]  Matthew W. Hoffman,et al.  Predictive Entropy Search for Efficient Global Optimization of Black-box Functions , 2014, NIPS.

[15]  Georges G. E. Gielen,et al.  Global optimization of integrated transformers for high frequency microwave circuits using a Gaussian process based surrogate model , 2011, 2011 Design, Automation & Test in Europe.

[16]  Georges G. E. Gielen,et al.  GASPAD: A General and Efficient mm-Wave Integrated Circuit Synthesis Method Based on Surrogate Model Assisted Evolutionary Algorithm , 2014, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[17]  Kirthevasan Kandasamy,et al.  Multi-fidelity Bayesian Optimisation with Continuous Approximations , 2017, ICML.

[18]  Kirthevasan Kandasamy,et al.  Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations , 2016, NIPS.

[19]  Matthias Poloczek,et al.  Multi-Information Source Optimization , 2016, NIPS.

[20]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[21]  Natalia Alexandrov,et al.  Multidisciplinary design optimization : state of the art , 1997 .

[22]  Peter Auer,et al.  Using Confidence Bounds for Exploitation-Exploration Trade-offs , 2003, J. Mach. Learn. Res..

[23]  Lihong Li,et al.  An Empirical Evaluation of Thompson Sampling , 2011, NIPS.

[24]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[25]  Xuan Zeng,et al.  An Efficient Bayesian Optimization Approach for Automated Optimization of Analog Circuits , 2018, IEEE Transactions on Circuits and Systems I: Regular Papers.

[26]  Rob A. Rutenbar,et al.  Anaconda: simulation-based synthesis of analog circuits viastochastic pattern search , 2000, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[27]  Warren B. Powell,et al.  The Correlated Knowledge Gradient for Simulation Optimization of Continuous Parameters using Gaussian Process Regression , 2011, SIAM J. Optim..

[28]  Fan Yang,et al.  Efficient multiple starting point optimization for automated analog circuit optimization via recycling simulation data , 2016, 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[29]  Michael T. M. Emmerich,et al.  Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels , 2006, IEEE Transactions on Evolutionary Computation.