Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.12076 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912331360894976 |
|---|---|
| author | Lu, Shao-Chien Yeh, Chen-Chen Cho, Hui-Lin Lin, Yu-Cheng Lin, Rung-Bin |
| author_facet | Lu, Shao-Chien Yeh, Chen-Chen Cho, Hui-Lin Lin, Yu-Cheng Lin, Rung-Bin |
| contents | We present a novel approach to solving the floorplanning problem by leveraging fine-tuned Large Language Models (LLMs). Inspired by subitizing--the human ability to instantly and accurately count small numbers of items at a glance--we hypothesize that LLMs can similarly address floorplanning challenges swiftly and accurately. We propose an efficient representation of the floorplanning problem and introduce a method for generating high-quality datasets tailored for model fine-tuning. We fine-tune LLMs on datasets with a specified number of modules to test whether LLMs can emulate the human ability to quickly count and arrange items. Our experimental results demonstrate that fine-tuned LLMs, particularly GPT4o-mini, achieve high success and optimal rates while attaining relatively low average dead space. These findings underscore the potential of LLMs as promising solutions for complex optimization tasks in VLSI design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12076 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Subitizing-Inspired_Large_Language_Models_for_Floorplanning Lu, Shao-Chien Yeh, Chen-Chen Cho, Hui-Lin Lin, Yu-Cheng Lin, Rung-Bin Hardware Architecture We present a novel approach to solving the floorplanning problem by leveraging fine-tuned Large Language Models (LLMs). Inspired by subitizing--the human ability to instantly and accurately count small numbers of items at a glance--we hypothesize that LLMs can similarly address floorplanning challenges swiftly and accurately. We propose an efficient representation of the floorplanning problem and introduce a method for generating high-quality datasets tailored for model fine-tuning. We fine-tune LLMs on datasets with a specified number of modules to test whether LLMs can emulate the human ability to quickly count and arrange items. Our experimental results demonstrate that fine-tuned LLMs, particularly GPT4o-mini, achieve high success and optimal rates while attaining relatively low average dead space. These findings underscore the potential of LLMs as promising solutions for complex optimization tasks in VLSI design. |
| title | Subitizing-Inspired_Large_Language_Models_for_Floorplanning |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2504.12076 |