arXiv cs.AI· Silicon Team·· 1 天前AI 评分38
如何评估 AI 智能体做工业级芯片布局规划：一个框架与实证研究
Evaluating AI Agents for Industrial-Level Chip Floorplanning: A Framework and Empirical Study
AI 导读
研究提出一套芯片布局规划评估框架，将智能体提交的布局接入指定下游实现流程，检查几何有效性并测量物理质量与资源消耗。在一个 99 个宏单元占据约 71% 固定 L 形区域的工业设计上，专用系统 Nova-SI 在无在线反馈条件下取得 57.97 的 0-100 综合质量均分，而表现最强的通用配置 GPT-6 Astra 为 31.17。
正文 · 原文
Abstract:Floorplanning is an early step in turning a logical circuit into a physical chip: it positions large circuit modules and constrains the space available for subsequent logic placement and wiring. These decisions affect congestion and timing, but their physical consequences emerge through downstream implementation. For AI agents, the task requires translating engineering constraints into spatial decisions whose quality cannot be judged by successful tool execution alone. We present an evaluation framework that connects submitted layouts to a prescribed downstream implementation flow, checks geometric validity, measures physical quality, and reports resource use. On one industrial design whose 99 macros occupy approximately 71\% of a fixed L-shaped layout region, we compare our specialized system Nova-SI with general-purpose engineering agents and agents restricted to file access, obtaining complete physical measurements for 39 layout outputs. Under our 0--100 composite quality score, Nova-SI achieves a mean of 57.97 without online feedback from the prescribed power-grid and placement evaluation, compared with 31.17 for the strongest measured general-purpose configuration, GPT-6 Astra. The difference is supported by raw congestion and timing measurements and persists across scoring-sensitivity checks. Layout-level analysis further shows that similar global utilization can conceal substantially different local congestion. Together, the protocol and case study assess agents through the physical consequences of their delivered layouts and provide evidence about system performance on this industrial instance.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.12048 [cs.AI] |
| (or arXiv:2611.12048v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.12048 arXiv-issued DOI via DataCite |
Submission history
From: Linyang Li [view email]
[v1]
Mon, 9 Nov 2026 08:25:58 UTC (5,276 KB)
[v2]
Thu, 12 Nov 2026 02:42:30 UTC (963 KB)
来源：arXiv cs.AI · arxiv.org