Summary of The Graph’s Apprentice: Teaching An Llm Low Level Knowledge For Circuit Quality Estimation, by Reza Moravej et al.
The Graph’s Apprentice: Teaching an LLM Low Level Knowledge for Circuit Quality Estimation
by Reza Moravej, Saurabh Bodhe, Zhanguang Zhang, Didier Chetelat, Dimitrios Tsaras, Yingxue Zhang, Hui-Ling Zhen, Jianye Hao, Mingxuan Yuan
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Computation and Language (cs.CL)
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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 proposes a novel approach to logic synthesis by augmenting large language models (LLMs) with predictor networks that estimate circuit quality from hardware description language (HDL) designs. The model is regularized using graph neural network (GNN) embeddings trained on Look-Up Table (LUT) graphs, incorporating lower-level circuit insights. The proposed method outperforms existing graph-based RTL-level estimation techniques on the OpenABCD benchmark, providing instant feedback on HDL code quality. This work has implications for the design of chips and microprocessors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper finds a new way to make computer chip designs better by using special language models. These models can look at the code that describes how the chip works and estimate how good it is. The model is made even better by using extra information from graphs of what the chip does. This new approach beats older methods for predicting how good a chip design is, which helps designers make better chips faster. |
Keywords
* Artificial intelligence * Gnn * Graph neural network
