Summary of Goalplace: Begin with the End in Mind, by Anthony Agnesina et al.
GOALPlace: Begin with the End in Mind
by Anthony Agnesina, Rongjian Liang, Geraldo Pradipta, Anand Rajaram, Haoxing Ren
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| 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 GOALPlace, a novel approach to optimizing placement congestion by controlling cell density. The method learns from an EDA tool’s post-route results using an empirical Bayes technique, adapting the goal/target to a specific placer’s solutions. This allows for efficient and effective placement optimization without requiring expensive incremental congestion estimation and mitigation methods. The paper also presents a statistical analysis that highlights the importance of density in placement and establishes the potential for an adequate cell density target across placements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GOALPlace is a new way to make electronic designs better by controlling how cells are placed on a chip. This approach learns from what’s already worked well in the past and uses that information to improve future designs. The method is very good at finding the best placement for cells, which helps reduce errors and makes the design work more efficiently. |
Keywords
* Artificial intelligence * Optimization
