تقرير
FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning
| العنوان: | FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning |
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| المؤلفون: | Wang, Shang, Mamillapalli, Deepak Ranganatha Sastry, Yang, Tianpei, Taylor, Matthew E. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Hardware Architecture, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | This paper introduces the problem of learning to place logic blocks in Field-Programmable Gate Arrays (FPGAs) and a learning-based method. In contrast to previous search-based placement algorithms, we instead employ Reinforcement Learning (RL) with the goal of minimizing wirelength. In addition to our preliminary learning results, we also evaluated a novel decomposition to address the nature of large search space when placing many blocks on a chipboard. Empirical experiments evaluate the effectiveness of the learning and decomposition paradigms on FPGA placement tasks. Comment: accepted by ISEDA2024 |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2404.13061Test |
| رقم الانضمام: | edsarx.2404.13061 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |