Summary of Make Every Move Count: Llm-based High-quality Rtl Code Generation Using Mcts, by Matthew Delorenzo et al.
Make Every Move Count: LLM-based High-Quality RTL Code Generation Using MCTS
by Matthew DeLorenzo, Animesh Basak Chowdhury, Vasudev Gohil, Shailja Thakur, Ramesh Karri, Siddharth Garg, Jeyavijayan Rajendran
First submitted to arxiv on: 5 Feb 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 addresses the limitations of existing large language models (LLMs) for register transfer level (RTL) code generation. Current LLMs often produce compilable, yet suboptimal power, performance, and area (PPA) efficient code due to a lack of PPA awareness in conventional transformer decoding algorithms. To overcome this challenge, the authors propose an automated transformer decoding algorithm that integrates Monte Carlo tree-search for lookahead, guiding the transformer to generate compilable, functionally correct, and PPA-optimized RTL code. Experimental results with a fine-tuned language model on RTL codesets demonstrate that this technique consistently generates functionally correct code compared to prompting-only methods, effectively addressing the PPA-unawareness drawback of naive LLMs. Notably, the proposed algorithm achieves a 31.8% improvement in area-delay product for the largest design generated by the state-of-the-art LLM (16-bit adder). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps computers generate better code, like instructions that a computer can understand and execute correctly. Currently, existing language models struggle to create good code because they don’t consider important factors like power consumption, processing speed, and chip size. To solve this problem, the authors developed an innovative algorithm that uses a “lookahead” approach to guide the language model in generating better code. The results show that their technique is more effective than previous methods, producing correct and efficient code for complex designs. |
Keywords
* Artificial intelligence * Language model * Prompting * Transformer
