Pith. sign in

REVIEW 4 cited by

LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2406.05250 v3 pith:SJQTBJE7 submitted 2024-06-07 cs.AI cs.ARcs.LG

Guojin Chen , Keren Zhu , Seunggeun Kim , Hanqing Zhu , Yao Lai , Bei Yu , David Z. Pan This is my paper · ORCID
classification cs.AIcs.ARcs.LG
keywords analoglayoutllanabayesianefficientgenerationlearningllms
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuits

    cs.LG 2024-11 conditional novelty 7.0 of 10

    LEDRO uses an LLM to propose refined parameter ranges, then runs TuRBO inside those ranges, beating full-space Bayesian optimization on a 22-topology, 4-node op-amp benchmark.

  2. LATENT: LLM-Augmented Trojan Insertion and Evaluation Framework for Analog Netlist Topologies

    cs.CR 2025-05 reject novelty 6.0 of 10

    LATENT uses an LLM agent with detection feedback to insert stealthy analog Trojans into SPICE netlists, reporting narrower activation ranges and higher evasion against the SPICED detector.

  3. Can an Actor-Critic Optimization Framework Improve Analog Design?

    cs.LG 2026-03 conditional novelty 5.0 of 10

    An actor-critic framework with two LLM agents—one proposing and one auditing search regions—improves analog sizing by 38.9% in top-10 FoM and 24.7% in regret over a single-LLM baseline.

  4. A Survey of Research in Large Language Models for Electronic Design Automation

    cs.LG 2025-01 conditional novelty 2.0 of 10

    A survey of LLM applications in electronic design automation, organized by design stage and adaptation technique.

Pith tools