Summary of Routeplacer: An End-to-end Routability-aware Placer with Graph Neural Network, by Yunbo Hou et al.
RoutePlacer: An End-to-End Routability-Aware Placer with Graph Neural Network
by Yunbo Hou, Haoran Ye, Yingxue Zhang, Siyuan Xu, Guojie Song
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces RoutePlacer, an end-to-end routability-aware placement method that jointly optimizes placement and routing. The approach utilizes a customized graph neural network (RouteGNN) trained to predict routability by capturing geometric and topological representations of placements. This allows for gradient-based optimization of routability during placement. Experiments on the DREAMPlace platform show RoutePlacer reduces Total Overflow by up to 16% while maintaining routed wirelength, outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RoutePlacer is a new way to design chips. Right now, designing chips involves two steps: placing tiny parts called transistors on the chip and connecting them with wires. The second step is called routing. Current ways of doing this don’t work well together. This paper makes things better by introducing RouteGNN, a special kind of computer program that can predict how good or bad a route will be. This helps make the whole process faster and more efficient. By using RouteGNN, chip designers can create chips that are up to 16% better than before. |
Keywords
» Artificial intelligence » Graph neural network » Optimization
