LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits
<|reference_start|>LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits: In the realm of electronic and electrical engineering, automation of analog circuit is increasingly vital given the complexity and customized requirements of modern applications. However, existing methods only develop search-based algorithms that require many simulation iterations to design a custom circuit topology, which is usually a time-consuming process. To this end, we introduce LaMAGIC, a pioneering language model-based topology generation model that leverages supervised finetuning for automated analog circuit design. LaMAGIC can efficiently generate an optimized circuit design from the custom specification in a single pass. Our approach involves a meticulous development and analysis of various input and output formulations for circuit. These formulations can ensure canonical representations of circuits and align with the autoregressive nature of LMs to effectively addressing the challenges of representing analog circuits as graphs. The experimental results show that LaMAGIC achieves a success rate of up to 96\% under a strict tolerance of 0.01. We also examine the scalability and adaptability of LaMAGIC, specifically testing its performance on more complex circuits. Our findings reveal the enhanced effectiveness of our adjacency matrix-based circuit formulation with floating-point input, suggesting its suitability for handling intricate circuit designs. This research not only demonstrates the potential of language models in graph generation, but also builds a foundational framework for future explorations in automated analog circuit design.<|reference_end|>
@article{chang2024lamagic:,
    title={LaMAGIC: Language-Model-based Topology Generation for Analog Integrated
  Circuits},
    author={Chen-Chia Chang, Yikang Shen, Shaoze Fan, Jing Li, Shun Zhang,
  Ningyuan Cao, Yiran Chen and Xin Zhang},
    journal={Proceedings of the 41st International Conference on Machine
  Learning, PMLR 235:6253-6262, 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18269},
    primaryClass={cs.AR cs.AI cs.LG}
}