Report
MazeNet: An Accurate, Fast, and Scalable Deep Learning Solution for Steiner Minimum Trees
| العنوان: | MazeNet: An Accurate, Fast, and Scalable Deep Learning Solution for Steiner Minimum Trees |
|---|---|
| المؤلفون: | Ramos, Gabriel Díaz, Arikan, Toros, Baraniuk, Richard G. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | The Obstacle Avoiding Rectilinear Steiner Minimum Tree (OARSMT) problem, which seeks the shortest interconnection of a given number of terminals in a rectilinear plane while avoiding obstacles, is a critical task in integrated circuit design, network optimization, and robot path planning. Since OARSMT is NP-hard, exact algorithms scale poorly with the number of terminals, leading practical solvers to sacrifice accuracy for large problems. We propose MazeNet, a deep learning-based method that learns to solve the OARSMT from data. MazeNet reframes OARSMT as a maze-solving task that can be addressed with a recurrent convolutional neural network (RCNN). A key hallmark of MazeNet is its scalability: we only need to train the RCNN blocks on mazes with a small number of terminals; larger mazes can be solved by replicating the same pre-trained blocks to create a larger network. Across a wide range of experiments, MazeNet achieves perfect OARSMT-solving accuracy, significantly reduces runtime compared to classical exact algorithms, and can handle more terminals than state-of-the-art approximate algorithms. Comment: 15 pages, 15 figures. Submitted to ICLR 2025 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2410.18832 |
| رقم الانضمام: | edsarx.2410.18832 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |