Summary of Improving Routability Prediction Via Nas Using a Smooth One-shot Augmented Predictor, by Arjun Sridhar et al.
Improving Routability Prediction via NAS Using a Smooth One-shot Augmented Predictor
by Arjun Sridhar, Chen-Chia Chang, Junyao Zhang, Yiran Chen
First submitted to arxiv on: 21 Nov 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 A novel Neural Architecture Search (NAS) technique, called SOAP-NAS, has been developed to optimize the performance of machine learning models in routability prediction for electronic design automation (EDA) tools. Traditional NAS methods struggle with this task due to noise introduced by the separation between training and search objectives, as well as increased variance. The SOAP-NAS approach addresses these challenges through data augmentation techniques and a combination of one-shot and predictor-based NAS. Experimental results show that SOAP-NAS outperforms existing solutions by 40% in DRC hotspot detection, achieving an ROC-AUC of 0.9802 with a query time of only 0.461 ms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make computer chips more efficient has been discovered using machine learning. This technique is called SOAP-NAS and helps create better models for predicting how well electronic designs will work. Right now, designing these chips is like trying to find a needle in a haystack – it takes a lot of trial and error. But with SOAP-NAS, it’s like having a special map that shows the best path to take. This new approach is faster and more accurate than what we had before, which means we can make better chips. |
Keywords
* Artificial intelligence * Auc * Data augmentation * Machine learning * One shot
