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

TL;DR

LaMAGIC2 introduces a more efficient and precise language model-based framework for analog topology generation, significantly improving success rates, accuracy, and transferability over previous methods.

Contribution

It presents a novel succinct float-input canonical formulation with identifier (SFCI) that enhances component recognition, reduces token complexity, and improves numeric sensitivity in analog topology design.

Findings

34% higher success rate under tight tolerance

10X lower mean squared errors compared to prior methods

Up to 58.5% improvement in transferability for larger circuits

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 of 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.