用更紧凑的格式提升大模型生成模拟电路的精度与效率
LaMAGIC2: Advanced Circuit Formulations for Language Model-Based Analog Topology Generation
- 设计新编码方式，将电路表示从平方级压缩为线性级
- 在0.01严苛容差下成功率提升34%，误差降低10倍
- 适合复杂电路生成，迁移能力更强，适用于高顶点电路
由于现代应用对定制化需求高，模拟电路拓扑设计自动化至关重要，但当前基于语言模型的方法依赖序列到序列架构和监督微调，其电路表示存在O(|V|²)的令牌长度，且对数值输入敏感度低。本文提出LaMAGIC2，一种简洁浮点输入规范形式（SFCI），通过标识符驱动的组件类型表示，将令牌长度复杂度降至O(|V|)，提升数值精度敏感性，在严苛容差下表现更优。实验表明，相比先前方法，LaMAGIC2在0.01容差下成功率提升34%，均方误差降低10倍，且在顶点数较多的电路中迁移性能提升达58.5%。该框架显著增强了模拟拓扑生成的鲁棒性。
原文摘要 · Abstract (English)
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.
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