Summary of Hivegen — Hierarchical Llm-based Verilog Generation For Scalable Chip Design, by Jinwei Tang et al.
HiVeGen – Hierarchical LLM-based Verilog Generation for Scalable Chip Design
by Jinwei Tang, Jiayin Qin, Kiran Thorat, Chen Zhu-Tian, Yu Cao, Yang, Zhao, Caiwen Ding
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
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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 HiVeGen, a framework that extends Large Language Models’ (LLMs) abilities to generate hierarchical Hardware Description Language (HDL) code for complex hardware designs like Domain-Specific Accelerators (DSAs). The existing LLM-based approach tends to generate single HDL blocks rather than hierarchical structures, leading to hallucinations. HiVeGen addresses this by decomposing generation tasks into manageable submodules and integrating automatic Design Space Exploration (DSE) into hierarchy-aware prompt generation. It also introduces weight-based retrieval for code reuse and real-time human-computer interaction to reduce error-correction costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a new language model better at generating code for designing special-purpose computer chips. Right now, these models just create small pieces of code rather than whole designs. This can lead to mistakes in the design. The researchers created a new way to use these models that breaks down the task into smaller parts and helps them work together more effectively. They also added ways to reuse old code and get help from humans when needed, which makes the generated designs better. |
Keywords
» Artificial intelligence » Language model » Prompt
