大海捞针：面向贝叶斯优化的测试时模拟电路表示自适应
Finding the Needle in a Haystack: Test-Time Analog Circuit Representation Adaptation for Bayesian Optimization
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- North Carolina State University（北卡罗来纳州立大学）
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中文总结 AI 辅助
本文提出TTARO框架，通过测试时自适应调整电路表示优化贝叶斯优化，在40种设置下较传统方法降低后悔曲线下面积15.2%至20.7%，性能优于现有技术。
中文摘要 AI 辅助
贝叶斯优化（Bayesian Optimization, BO）是一种针对模拟电路拓扑搜索的样本高效框架，评估每个候选拓扑可能需要代价高昂的仿真。然而，基于表示的贝叶斯优化方法通常在编码器训练后将电路嵌入视为固定的，这造成了表示学习与优化之间的不匹配：用于编码或重构电路结构的嵌入不一定按照要优化的性能指标（Figure of Merit, FoM）进行组织。本文提出了面向贝叶斯优化的测试时模拟电路表示自适应（Test-Time Analog Representation Adaptation for Bayesian Optimization, TTARO），这是一种在线深度核贝叶斯优化框架，可在整个搜索过程中自适应调整电路表示。从预训练的电路嵌入开始，TTARO利用迄今为止评估过的电路的性能指标标签，联合学习非线性特征变换和高斯过程代理模型。每次新评估后，TTARO会更新表示和代理模型，然后选择下一个候选。我们将TTARO与基于固定嵌入的传统高斯过程贝叶斯优化，以及仅从初始评估设计中学习表示并在搜索剩余过程中保持固定的深度核学习（Deep Kernel Learning, DKL）进行比较。通过在表示学习中持续纳入新观察到的性能指标标签，TTARO在贝叶斯优化推进过程中使搜索空间与优化目标对齐。在实验中，TTARO在40种编码器/核/采集设置下，相对于贝叶斯优化平均降低了15.2%的后悔曲线下面积（regret AUC），相对于DKL平均降低了20.7%，在大多数设置下优于现有技术，最大降低幅度达46.7%。
英文摘要
Bayesian optimization (BO) is a sample-efficient framework for analog circuit topology search, where evaluating each candidate topology can require costly simulation. However, representation-based BO methods typically treat circuit embeddings as fixed after encoder training. This creates a mismatch between representation learning and optimization: embeddings learned to encode or reconstruct circuit structure are not necessarily organized according to the figure of merit (FoM) being optimized. This paper introduces Test-Time Analog Representation Adaptation for Bayesian Optimization (TTARO), an online deep-kernel BO framework that adapts circuit representations throughout the search process. Starting from pretrained circuit embeddings, TTARO jointly learns a nonlinear feature transformation and a Gaussian-process surrogate using the FoM labels of the circuits evaluated so far. Following each new evaluation, TTARO updates the representation and surrogate before selecting the next candidate. We compare TTARO with conventional Gaussian Process-based BO over fixed embeddings and with Deep Kernel Learning (DKL), which learns the representation only from the initial evaluated designs and keeps it fixed throughout the remainder of the search. By continually incorporating newly observed FoM labels into representation learning, TTARO aligns the search space with the optimization objective as BO progresses. In our experiments, TTARO reduces regret AUC by 15.2% on average relative to BO and by 20.7% relative to DKL across 40 encoder/kernel/acquisition settings, outperforming prior art in most settings with reductions as large as 46.7%.
