IPLAM: A High-Dimensional Expensive Simulation Optimization Method, With Application to Design Space Exploration in Processor
Design Space Exploration (DSE) in processors is an expensive discrete simulation optimization problem. The data requirements of the regular data-driven methods are so large that it is challenging to converge to a satisfactory solution within a limited simulation budget. Based on binary integer linea...
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| Veröffentlicht in: | IEEE transactions on automation science and engineering Jg. 22; S. 18761 - 18772 |
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| Abstract | Design Space Exploration (DSE) in processors is an expensive discrete simulation optimization problem. The data requirements of the regular data-driven methods are so large that it is challenging to converge to a satisfactory solution within a limited simulation budget. Based on binary integer linear programming (BILP), an iteratively piecewise linear approximate method (IPLAM) is proposed for this kind of problems to reduce the dependence of simulation data. IPLAM starts from an initial reference point. Each iteration generates a set of trial points based on the most promising reference point by piecewise shift method. After evaluating the trial points, a local surrogate model is constructed for the unit neighborhood of the reference point. The surrogate model is then used to guide the exploration of the next reference point. In theory, IPLAM can converge to the global optimal point under mild assumptions, which is further verified by the numerical experiments. Meanwhile, the numerical experiments demonstrate that IPLAM outperforms the advanced Bayesian optimization and differential evolution methods on high-dimensional discrete closed-box test functions. Besides, the practical effectiveness of IPLAM is validated by an industrial case for processor DSE. Note to Practitioners-This paper proposes an efficient optimization method, IPLAM, for high-dimensional expensive simulation problems. To cope with the extremely limited simulation data, we construct the surrogate objective for the local space around the reference point by evaluating a set of trail points. The surrogate objective is then based on to locate the most promising point as the next reference point. We also provide theoretical guarantees of convergence for the proposed method. According to the numerical experiments, the proposed method demonstrates a much superior exploration efficiency and a lower simulation data requirement than competing methods. Besides, compared with an advanced Bayesian optimization method (HEBO) in an industrial case of processor DSE, the proposed method achieves a 47% improvement in convergence speed and a 15% reduction in simulation overhead while the difference of simulation score is marginal. |
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| AbstractList | Design Space Exploration (DSE) in processors is an expensive discrete simulation optimization problem. The data requirements of the regular data-driven methods are so large that it is challenging to converge to a satisfactory solution within a limited simulation budget. Based on binary integer linear programming (BILP), an iteratively piecewise linear approximate method (IPLAM) is proposed for this kind of problems to reduce the dependence of simulation data. IPLAM starts from an initial reference point. Each iteration generates a set of trial points based on the most promising reference point by piecewise shift method. After evaluating the trial points, a local surrogate model is constructed for the unit neighborhood of the reference point. The surrogate model is then used to guide the exploration of the next reference point. In theory, IPLAM can converge to the global optimal point under mild assumptions, which is further verified by the numerical experiments. Meanwhile, the numerical experiments demonstrate that IPLAM outperforms the advanced Bayesian optimization and differential evolution methods on high-dimensional discrete closed-box test functions. Besides, the practical effectiveness of IPLAM is validated by an industrial case for processor DSE. Note to Practitioners-This paper proposes an efficient optimization method, IPLAM, for high-dimensional expensive simulation problems. To cope with the extremely limited simulation data, we construct the surrogate objective for the local space around the reference point by evaluating a set of trail points. The surrogate objective is then based on to locate the most promising point as the next reference point. We also provide theoretical guarantees of convergence for the proposed method. According to the numerical experiments, the proposed method demonstrates a much superior exploration efficiency and a lower simulation data requirement than competing methods. Besides, compared with an advanced Bayesian optimization method (HEBO) in an industrial case of processor DSE, the proposed method achieves a 47% improvement in convergence speed and a 15% reduction in simulation overhead while the difference of simulation score is marginal. |
| Author | Lv, Xiaoliang Guan, Xiaohong Hu, Jianchen Zhai, Qiaozhu Zhu, Yuhang |
| Author_xml | – sequence: 1 givenname: Xiaoliang orcidid: 0009-0008-6565-6701 surname: Lv fullname: Lv, Xiaoliang email: xiaoliang.lv@stu.xjtu.edu.cn organization: Systems Engineering Institute, MOE KLINNS Lab, Xi'an Jiaotong University, Xi'an, China – sequence: 2 givenname: Qiaozhu orcidid: 0000-0002-7312-4923 surname: Zhai fullname: Zhai, Qiaozhu email: qzzhai@sei.xjtu.edu.cn organization: Systems Engineering Institute, MOE KLINNS Lab, Xi'an Jiaotong University, Xi'an, China – sequence: 3 givenname: Yuhang orcidid: 0009-0003-1282-3805 surname: Zhu fullname: Zhu, Yuhang email: zhuyh21@mails.tsinghua.edu.cn organization: Center for Intelligent and Networked Systems and TNLIST, Tsinghua University, Beijing, China – sequence: 4 givenname: Jianchen orcidid: 0000-0002-4143-9955 surname: Hu fullname: Hu, Jianchen email: horace89@xjtu.edu.cn organization: Systems Engineering Institute, MOE KLINNS Lab, Xi'an Jiaotong University, Xi'an, China – sequence: 5 givenname: Xiaohong orcidid: 0000-0002-8826-0362 surname: Guan fullname: Guan, Xiaohong email: xhguan@sei.xjtu.edu.cn organization: Systems Engineering Institute, MOE KLINNS Lab, Xi'an Jiaotong University, Xi'an, China |
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| SubjectTerms | Automation Bayes methods binary integer linear programming Closed box Convergence Costs Data models design space exploration Expensive simulation system high-dimensional discrete optimization Optimization methods Piecewise linear approximation Predictive models Space exploration |
| Title | IPLAM: A High-Dimensional Expensive Simulation Optimization Method, With Application to Design Space Exploration in Processor |
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