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LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation

Guojin Chen, Keren Zhu, Seunggeun Kim, Hanqing Zhu, Yao Lai, Bei Yu, David Z. Pan

arXiv:2406.05250v3cs.AIcs.ARcs.LG

TL;DR

Analog layout synthesis remains manual and time-consuming, while BO faces challenges in high-dimensional optimization and learning objectives from limited data. LLANA uses LLMs to enhance BO constraint generation and candidate sampling; the authors report improved performance with limited samples and slightly better results on CMRR and Offset benchmarks than existing techniques.

  • Problem

    Analog layout remains manual and time-consuming, while design-specific constraints lack adaptability and BO faces difficulty learning objectives efficiently in few-shot settings.

    Source: §I · p. 1

  • Method

    LLANA augments BO with LLM-based surrogate modeling through in-context learning and conditional generation of candidate points for target objective values.

    Source: C. LLM-enhanced BO Framework · p. 3, §V · p. 4

  • Results

    LLANA improves performance across three integrations, particularly with limited samples, and shows slightly better results on CMRR and Offset benchmarks than existing techniques.

    Source: §V · p. 4

  • Takeaways & Limitations

    The findings suggest LLANA trades higher computational cost from LLM inference for improved sample efficiency in black-box optimization.

    Source: §V · p. 4

    The study focuses on single-objective BO; multi-objective and higher-dimensional tasks remain future work.

    Source: §V · p. 4

Abstract

from arXiv · show

Analog layout synthesis faces significant challenges due to its dependence on manual processes, considerable time requirements, and performance instability. Current Bayesian Optimization (BO)-based techniques for analog layout synthesis, despite their potential for automation, suffer from slow convergence and extensive data needs, limiting their practical application. This paper presents the \texttt{LLANA} framework, a novel approach that leverages Large Language Models (LLMs) to enhance BO by exploiting the few-shot learning abilities of LLMs for more efficient generation of analog design-dependent parameter constraints. Experimental results demonstrate that \texttt{LLANA} not only achieves performance comparable to state-of-the-art (SOTA) BO methods but also enables a more effective exploration of the analog circuit design space, thanks to LLM's superior contextual understanding and learning efficiency. The code is available at https://github.com/dekura/LLANA.

I. INTRODUCTION

Analog layout synthesis remains manual, time-consuming, and difficult to predict accurately as layout-dependent effects grow more complex. LLANA investigates whether LLM few-shot learning can improve BO for generating design-dependent layout constraints.

I. INTRODUCTION

  • Analog layout creation is manual, time-consuming, and error-prone, while growing layout-dependent effects complicate post-layout performance prediction.Attempts to automate the process have had limited integration into real-world design practices.Source: §I · p. 1
  • Prior optimization-driven tools require manually specified constraints, while heuristic constraints lack adaptability and analytical estimates become less accurate with device miniaturization.Accurately predicting mismatch and parasitic effects in scaled technologies requires extensive empirical simulation.Source: §I · p. 1
  • Design-dependent P&R hyperparameters such as net weighting remain underexplored, despite prior work on matching constraints such as symmetry.BO also faces high-dimensional computational demands and local optima, with few-shot learning highlighting the challenge of learning objectives from minimal data.Source: §I · p. 1
  • LLANA investigates LLM-enhanced BO for generating and fine-tuning analog design-dependent parameters, using natural-language representations of BO components.The paper also formulates the parameter space and provides a Gaussian-process BO benchmark.Source: §I · p. 2

II. PRELIMINARIES AND BACKGROUND

Analog layout synthesis combines placement and routing, with placement optimizing component locations and routing completing connections. Its global-placement objective balances wirelength, layout area, and other objectives.

A. Analog placement

  • Placement optimizes component locations within a bounding box to minimize wirelength; routing finalizes the layout using component positions, pin locations, and connection data.The synthesis process also respects symmetry constraints to minimize mismatches.Source: A. Analog placement · p. 2
  • The analytical global-placement objective f combines weighted net wirelength, total layout area, and other necessary objectives.The placement and routing engine is described as design-independent.Source: A. Analog placement · p. 2
  • In the objective, α_i and β denote net weightings and the area-minimization factor, while f_WL_i, f_AREA, and f_other denote wirelength, total area, and other objectives.Source: A. Analog placement · p. 2

B. Analog design-dependent parameter constraint:

Design-dependent parameters and hyperparameters shape analog layout efficiency and performance. This paper focuses on net weightings, which influence global placement through their effect on wirelength and parasitics.

B. Analog design-dependent parameter constraint:

A. BO-based methods as baselines

The baseline methods formulate analog constraint generation as Bayesian optimization, including multi-objective BO with Gaussian-process surrogate models. They iteratively evaluate design-specific layout parameters and use the resulting metrics to select the next sample.

A. BO-based methods as baselines

  • Prior analog constraint-generation work primarily uses multi-objective Bayesian optimization with Gaussian-process surrogate models.Source: A. BO-based methods as baselines · p. 2
  • The formulation minimizes multiple post-layout performance metrics obtained by simulation after automatic layout generation using Magical.The design-specific layout parameters are the optimization variables.Source: A. BO-based methods as baselines · p. 2
  • MOBO iteratively samples design points and their performance metrics, updates Gaussian-process models, and optimizes an acquisition function to obtain the next net weighting.Source: A. BO-based methods as baselines · p. 2

B. Large Language Models (LLMs)-enhanced BO

LLMs are presented as a way to address BO’s limited-data and prior-knowledge challenges through in-context learning and contextual processing.

B. Large Language Models (LLMs)-enhanced BO

  • BO is challenged by limited data, model sensitivity, and difficulty incorporating prior knowledge into new tasks.LLMs are proposed as a source of priors and generalization from limited examples for efficient exploration.Source: B. Large Language Models… · p. 2
  • LLM in-context learning is described as implicit Bayesian inference, supporting the use of pretrained insights in BO.The passage also identifies generalization from few examples and contextual processing as ways LLMs can enrich optimization and search.Source: B. Large Language Models… · p. 2
  • LLMs can process contextual information to enrich optimization tasks and search strategies.The passage presents this alongside prior knowledge and few-example generalization as potential ways to enhance BO.Source: B. Large Language Models… · p. 2

C. LLM-enhanced BO Framework

LLANA combines LLM-generated initial designs, an in-context surrogate, and conditional candidate generation in an iterative BO procedure.

C. LLM-enhanced BO Framework

  • LLANA generates initial designs using LLM zero-shot capabilities and prompt engineering.The passage says this is intended to produce better initial designs.Source: C. LLM-enhanced BO Framework · p. 2, Figure 2 · p. 2
  • The surrogate serializes the optimization trajectory as natural text, then the LLM predicts a score and associated probability from that text and queried examples.A shuffling mechanism randomly permutes few-shot examples to enhance robustness.Source: C. LLM-enhanced BO Framework · p. 3
  • LLANA conditionally generates candidate points through in-context learning using a desired objective value and observed data.It generates candidates independently and selects the point maximizing the acquisition function for evaluation next.Source: C. LLM-enhanced BO Framework · p. 3, Figure 4 · p. 3
  • The end-to-end procedure samples candidates through ICL, scores them with the ICL surrogate, and evaluates the candidate with the highest acquisition score.LLANA uses expected improvement as its acquisition function.Source: C. LLM-enhanced BO Framework · p. 3

IV. EXPERIMENTS

Experiments use LLANA with a two-stage operational amplifier design and evaluate CMRR and Offset objectives on a prepared dataset.

IV. EXPERIMENTS

  • Experiments use two-stage operational amplifiers with 36 devices and 14 design-dependent critical-net-weighting parameters.The parameters are selected by the MOBO algorithm.Source: §IV · p. 3
  • The dataset contains 500 design-performance pairs, split into 400 training pairs and 100 testing pairs.The objectives are common-mode rejection ratio (CMRR) and absolute input-referred offset (Offset) voltage.Source: §IV · p. 3
  • The experiments use gpt-3.5-turbo with α = −0.1 and M = 20, and evaluate performance using MSE with expected improvement.Post-layout performance is evaluated by Cadence Spectre after layout generation using Magical.Source: §IV · p. 3

A. Evaluation of the surrogate model

On CMRR, LLANA outperforms GP and SMAC on prediction metrics and achieves lower LPD, while on Offset its prediction scores approach theirs at 30 observed points.

A. Evaluation of the surrogate model

  • LLANA outperforms GP and SMAC on CMRR NRMSE and R2, with lower normalized regret when n < 15 and lower LPD.The comparison is shown in Fig. 5; lower LPD is reported as the best uncertainty quantification.Source: Figure 5 · p. 4, A. Evaluation of the surrogate model · p. 4
  • On Offset, LLANA’s NRMSE and R2 scores become close to GP and SMAC at n = 30, while its regret and LPD remain ahead.The passage also reports that LLANA excels earlier in search when fewer observations are available.Source: A. Evaluation of the surrogate model · p. 4
  • Permuting few-shot examples is reported to improve both uncertainty quantification and prediction performance.The paper identifies both as critical aspects of balancing exploration and exploitation.Source: A. Evaluation of the surrogate model · p. 4
  • The ICL surrogate can provide regression estimates with uncertainty, but stronger prediction performance trades off against worse calibration than probabilistic methods.The paper also identifies the LLM’s encoded prior as crucial to surrogate-model efficacy.Source: A. Evaluation of the surrogate model · p. 4

V. CONCLUSION AND DISCUSSION

LLANA combines LLMs with BO to generate analog net-weighting constraints through ICL-based surrogate models and conditional candidate sampling. The study reports improved performance, especially with limited samples, and slightly better standalone BO results on CMRR and Offset, while noting higher inference cost and limited scope.

  • LLANA integrates LLMs with BO for analog net-weighting constraints, using ICL surrogate models and conditional candidate sampling.Source: §V · p. 4
  • Performance improvements across the three integrations are particularly evident with limited sample sizes.Source: §V · p. 4
  • LLANA shows potential as a standalone BO method, with slightly better results on CMRR and Offset benchmarks than existing techniques.Source: Figure 6 · p. 4, §V · p. 4
  • LLANA incurs higher LLM-inference cost, remains single-objective, and needs broader evaluation across multi-objective and higher-dimensional tasks.Source: §V · p. 4

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