Summary of Mtlso: a Multi-task Learning Approach For Logic Synthesis Optimization, by Faezeh Faez et al.
MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization
by Faezeh Faez, Raika Karimi, Yingxue Zhang, Xing Li, Lei Chen, Mingxuan Yuan, Mahdi Biparva
First submitted to arxiv on: 9 Sep 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 Multi-Task Learning approach for Logic Synthesis Optimization (MTLSO) addresses challenges in predicting Quality of Results (QoR) for And-Inverter Graphs (AIGs) and synthesis recipes in Electronic Design Automation (EDA). The scarcity of data leads to overfitting, while the complexity of AIGs hinders traditional graph neural networks. MTLSO maximizes limited data by training across different tasks, including binary multi-label graph classification, and employs hierarchical graph representation learning for expressive representations. Experimental results demonstrate an average performance gain of 8.22% for delay and 5.95% for area against state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Logic synthesis is a crucial step in Electronic Design Automation (EDA) that transforms high-level hardware descriptions into optimized netlists. Researchers have used machine learning to predict Quality of Results (QoR) for And-Inverter Graphs (AIGs) and synthesis recipes, but this work has been hindered by limited data and complex AIGs. To solve these problems, a new approach called MTLSO is proposed. This approach uses multiple tasks, including classification, to help the model learn from limited data and create better representations of large AIGs. The results show that MTLSO outperforms other methods in predicting delay and area. |
Keywords
» Artificial intelligence » Classification » Machine learning » Multi task » Optimization » Overfitting » Representation learning
