用大模型自动进化优化算法，显著提升电路布局性能。
Evolution of Optimization Algorithms for Global Placement via Large Language Models
- 用大模型生成并演化优化算法，替代人工设计。
- 在多个基准上平均降低5.05%~8.30%的HPWL，个别案例提升17%。
- 结果可泛化且与调参方法互补，适合EDA算法研发者。
优化算法广泛用于解决复杂问题，但手动设计耗时且需专业知识。全局布局是电子设计自动化（EDA）的基础步骤。尽管解析方法代表当前最优（SOTA），其核心优化算法仍严重依赖启发式策略和定制组件，如初始化、预条件化和线搜索技术。本文提出一种基于大语言模型（LLM）的自动化框架，用于演化全局布局的优化算法。首先通过精心设计的提示词生成多样候选算法；随后引入基于LLM的遗传流程，对选中算法进行演化。所发现的优化算法在多个基准测试中表现显著提升：在MMS、ISPD2005和ISPD2019基准上，设计特定算法平均实现HPWL降低5.05%、5.29%和8.30%，个别案例最高达17%。此外，这些算法展现出良好泛化能力，且与现有参数调优方法具有互补性。
原文摘要 · Abstract (English)
Optimization algorithms are widely employed to tackle complex problems, but designing them manually is often labor-intensive and requires significant expertise. Global placement is a fundamental step in electronic design automation (EDA). While analytical approaches represent the state-of-the-art (SOTA) in global placement, their core optimization algorithms remain heavily dependent on heuristics and customized components, such as initialization strategies, preconditioning methods, and line search techniques. This paper presents an automated framework that leverages large language models (LLM) to evolve optimization algorithms for global placement. We first generate diverse candidate algorithms using LLM through carefully crafted prompts. Then we introduce an LLM-based genetic flow to evolve selected candidate algorithms. The discovered optimization algorithms exhibit substantial performance improvements across many benchmarks. Specifically, Our design-case-specific discovered algorithms achieve average HPWL improvements of \textbf{5.05\%}, \text{5.29\%} and \textbf{8.30\%} on MMS, ISPD2005 and ISPD2019 benchmarks, and up to \textbf{17\%} improvements on individual cases. Additionally, the discovered algorithms demonstrate good generalization ability and are complementary to existing parameter-tuning methods.
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