AIvril: AI-Driven RTL Generation With Verification In-The-Loop
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914951607615488 |
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| author | Islam, Mubashir ul Sami, Humza Gaillardon, Pierre-Emmanuel Tenace, Valerio |
| author_facet | Islam, Mubashir ul Sami, Humza Gaillardon, Pierre-Emmanuel Tenace, Valerio |
| contents | Large Language Models (LLMs) are computational models capable of performing complex natural language processing tasks. Leveraging these capabilities, LLMs hold the potential to transform the entire hardware design stack, with predictions suggesting that front-end and back-end tasks could be fully automated in the near future. Currently, LLMs show great promise in streamlining Register Transfer Level (RTL) generation, enhancing efficiency, and accelerating innovation. However, their probabilistic nature makes them prone to inaccuracies - a significant drawback in RTL design, where reliability and precision are essential.
To address these challenges, this paper introduces AIvril, an advanced framework designed to enhance the accuracy and reliability of RTL-aware LLMs. AIvril employs a multi-agent, LLM-agnostic system for automatic syntax correction and functional verification, significantly reducing - and in many cases, completely eliminating - instances of erroneous code generation. Experimental results conducted on the VerilogEval-Human dataset show that our framework improves code quality by nearly 2x when compared to previous works, while achieving an 88.46% success rate in meeting verification objectives. This represents a critical step toward automating and optimizing hardware design workflows, offering a more dependable methodology for AI-driven RTL design. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_11411 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AIvril: AI-Driven RTL Generation With Verification In-The-Loop Islam, Mubashir ul Sami, Humza Gaillardon, Pierre-Emmanuel Tenace, Valerio Artificial Intelligence Hardware Architecture Computation and Language Machine Learning Multiagent Systems Large Language Models (LLMs) are computational models capable of performing complex natural language processing tasks. Leveraging these capabilities, LLMs hold the potential to transform the entire hardware design stack, with predictions suggesting that front-end and back-end tasks could be fully automated in the near future. Currently, LLMs show great promise in streamlining Register Transfer Level (RTL) generation, enhancing efficiency, and accelerating innovation. However, their probabilistic nature makes them prone to inaccuracies - a significant drawback in RTL design, where reliability and precision are essential. To address these challenges, this paper introduces AIvril, an advanced framework designed to enhance the accuracy and reliability of RTL-aware LLMs. AIvril employs a multi-agent, LLM-agnostic system for automatic syntax correction and functional verification, significantly reducing - and in many cases, completely eliminating - instances of erroneous code generation. Experimental results conducted on the VerilogEval-Human dataset show that our framework improves code quality by nearly 2x when compared to previous works, while achieving an 88.46% success rate in meeting verification objectives. This represents a critical step toward automating and optimizing hardware design workflows, offering a more dependable methodology for AI-driven RTL design. |
| title | AIvril: AI-Driven RTL Generation With Verification In-The-Loop |
| topic | Artificial Intelligence Hardware Architecture Computation and Language Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2409.11411 |