High Dimensional Optimization for Electronic Design

High Dimensional Optimization for Electronic Design
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DOI:
10.1145/3551901.3556495
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发表时间:
2022-09
期刊:
2022 ACM/IEEE 4th Workshop on Machine Learning for CAD (MLCAD)
影响因子:
--
通讯作者:
Yuejiang Wen;J. Dean;Brian A. Floyd;P. Franzon
Yuejiang Wen;J. Dean;Brian A. Floyd;P. Franzon
中科院分区:
其他
文献类型:
--
作者:
Yuejiang Wen;J. Dean;Brian A. Floyd;P. Franzon

文献摘要

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贝叶斯优化(BO)对兴趣点进行采样,以更新黑箱函数的代理模型。这使得它成为优化目标函数未知、仿真计算成本高的电子设计的有力技术。不幸的是,贝叶斯优化受到可伸缩性问题的困扰,例如,它可以在多达20维的问题中表现良好。针对维数问题,提出了一种基于检查的组合随机嵌入贝叶斯优化算法(IC-REMBO)。IC-REMBO提高了随机嵌入贝叶斯优化(REMBO)方法的有效性和效率,这是目前最先进的高维优化方法。一般通过考察局部最优点附近的空间来探索更多的局部最优点附近的点,从而减轻了REMBO中边界上的过度探索和嵌入失真。因此,它有助于摆脱局部最优,并在有限的迭代次数内检查接近全局最优时提供了一系列可行的解决方案。在优化具有38个校准参数以满足4个目标的毫米波接收机时,将所提出算法的有效性和效率与最先进的REMBO进行了比较。优化结果接近人类专家的结果。据我们所知,这是第一次将REMBO或检验方法应用到电子设计中。
Bayesian optimization (BO) samples points of interest to update a surrogate model for a blackbox function. This makes it a powerful technique to optimize electronic designs which have unknown objective functions and demand high computational cost of simulation. Unfortunately, Bayesian optimization suffers from scalability issues, e.g., it can perform well in problems up to 20 dimensions. This paper addresses the curse of dimensionality and proposes an algorithm entitled Inspection-based Combo Random Embedding Bayesian Optimization (IC-REMBO). IC-REMBO improves the effectiveness and efficiency of the Random EMbedding Bayesian Optimization (REMBO) approach, which is a state-of-the-art high dimensional optimization method. Generally, it inspects the space near local optima to explore more points near local optima, so that it mitigates the over-exploration on boundaries and embedding distortion in REMBO. Consequently, it helps escape from local optima and provides a family of feasible solutions when inspecting near global optimum within a limited number of iterations.The effectiveness and efficiency of the proposed algorithm are compared with the state-of-the-art REMBO when optimizing a mmWave receiver with 38 calibration parameters to meet 4 objectives. The optimization results are close to that of a human expert. To the best of our knowledge, this is the first time applying REMBO or inspection method to electronic design.