🤖 AI Summary
In traditional Bayesian optimization, fixed circuit embeddings often fail to align structural representations with figures of merit (FoM), thereby limiting search efficiency. This work proposes TTARO, a novel framework that introduces test-time representation adaptation for the first time in analog circuit topology search. Within the Bayesian optimization loop, TTARO jointly learns nonlinear feature transformations and a Gaussian process surrogate model online, dynamically adjusting circuit embeddings to continuously align with the optimization objective. By integrating pretrained embeddings, deep kernel learning, and Gaussian process regression, the method enables co-evolution of embeddings and the target FoM. Experiments across 40 configurations demonstrate that TTARO reduces the average simple regret AUC by 15.2% compared to conventional Bayesian optimization and outperforms deep kernel learning baselines by 20.7%, with improvements reaching up to 46.7% in certain scenarios.
📝 Abstract
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%.