Taku Ito, Luca Cocchi, et al.
ICML 2025
Automation of analog topology design is crucial due to customized requirements of modern appli- cations 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 ana- log topology generation. SFCI addresses these challenges by improving component-type recog- nition through identifier-based representations, re- ducing token length complexity to O(|V | + |E|), and enhancing numeric precision sensitivity for better performance under tight tolerances. Our ex- periments 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 transfer- ability for circuits with more vertices with up to 58.5% improvement. These advancements estab- lish LaMAGIC2 as a robust framework for analog topology generation.
Taku Ito, Luca Cocchi, et al.
ICML 2025
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
Robert Farrell, Rajarshi Das, et al.
AAAI-SS 2010