LaMAGIC2: Advanced Circuit Formulations for Language Model-Based Analog Topology Generation

Chen-Chia Chang, Wan-Hsuan Lin, Yikang Shen, Yiran Chen, Xin Zhang

Abstract:

Automation of analog topology design is crucial due to customized requirements of modern applications with heavily manual engineering efforts.
The state-of-the-art work applies a sequence-to-sequence approach and supervised finetuning on language models to generate topologies given user specifications.
However, its circuit formulation is inefficient due to $O(|V|^2)$ token length and suffers from low precision sensitivity to numeric inputs.
In this work, we introduce LaMAGIC2, a succinct float-input canonical formulation
with identifier (SFCI) for language model-based analog topology generation.
SFCI addresses these challenges by improving component-type recognition through identifier-based representations, reducing token length complexity to $O(|V|)$, and enhancing numeric precision sensitivity for better performance under tight tolerances.
Our experiments demonstrate that LaMAGIC2 achieves 34\% higher success rates under a tight tolerance 0.01 and 10X lower MSEs compared to a prior method.
LaMAGIC2 also exhibits better transferability for circuits with more vertices with up to 58.5\% improvement.
These advancements establish LaMAGIC2 as a robust framework for analog topology generation.

Lay Summary: Automation of analog topology design is crucial due to customized requirements of modern applications with heavily manual engineering efforts. The state-of-the-art work applies a sequence-to-sequence approach and supervised finetuning on language models to generate topologies given user specifications. However, its circuit formulation is inefficient due to O(|V|^2) token length and suffers from low precision sensitivity to numeric inputs. Thus, we introduce LaMAGIC2, a succinct float-input canonical formulation with identifier (SFCI) for language model-based analog topology generation. SFCI addresses these challenges by improving component-type recognition through identifier-based representations, reducing token length complexity to O(|V|), and enhancing numeric precision sensitivity for better performance under tight tolerances. Our step-by-step analysis of circuit formulations provides valuable insights into graph generation with transformer models, advancing the field of topology generation and beyond.

Application-Driven Machine Learning: This submission is on Application-Driven Machine Learning.

Primary Area: Applications->Everything Else

Keywords: Analog Circuit Automation, Language Model

Submission Number: 7452