Summary of Optimizing Predictive Ai in Physical Design Flows with Mini Pixel Batch Gradient Descent, by Haoyu Yang and Anthony Agnesina and Haoxing Ren
Optimizing Predictive AI in Physical Design Flows with Mini Pixel Batch Gradient Descent
by Haoyu Yang, Anthony Agnesina, Haoxing Ren
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed mini-pixel batch gradient descent (MPGD) algorithm optimizes model training for chip physical design flows by considering only informative entries, leading to faster and better convergence. State-of-the-art frameworks typically focus on minimizing the mean square error (MSE) between predictions and ground truth, but this approach has limitations. MPGD addresses these issues by offering a plug-and-play optimization solution that improves model performance in real-world scenarios. The algorithm is demonstrated to provide significant benefits for various physical design prediction tasks using CNN or Graph-based models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of training AI models makes it easier and more accurate to predict chip designs. Normally, AI tries to get as close as possible to the correct answer, but this can be a problem because small mistakes can have big effects. To fix this, researchers came up with a new algorithm called mini-pixel batch gradient descent (MPGD). MPGD is better at training models and helps them make more accurate predictions. It works by only considering important information when updating the model’s weights. This leads to faster and more reliable results. |
Keywords
* Artificial intelligence * Cnn * Gradient descent * Mse * Optimization
