Using LLM such as ChatGPT for Designing and Implementing a RISC Processor: Execution,Challenges and Limitations
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913200232988672 |
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| author | Hossain, Shadeeb Gohil, Aayush Wang, Yizhou |
| author_facet | Hossain, Shadeeb Gohil, Aayush Wang, Yizhou |
| contents | This paper discusses the feasibility of using Large Language Models LLM for code generation with a particular application in designing an RISC. The paper also reviews the associated steps such as parsing, tokenization, encoding, attention mechanism, sampling the tokens and iterations during code generation. The generated code for the RISC components is verified through testbenches and hardware implementation on a FPGA board. Four metric parameters Correct output on the first iteration, Number of errors embedded in the code, Number of trials required to achieve the code and Failure to generate the code after three iterations, are used to compare the efficiency of using LLM in programming. In all the cases, the generated code had significant errors and human intervention was always required to fix the bugs. LLM can therefore be used to complement a programmer code design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10364 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using LLM such as ChatGPT for Designing and Implementing a RISC Processor: Execution,Challenges and Limitations Hossain, Shadeeb Gohil, Aayush Wang, Yizhou Machine Learning Hardware Architecture Software Engineering This paper discusses the feasibility of using Large Language Models LLM for code generation with a particular application in designing an RISC. The paper also reviews the associated steps such as parsing, tokenization, encoding, attention mechanism, sampling the tokens and iterations during code generation. The generated code for the RISC components is verified through testbenches and hardware implementation on a FPGA board. Four metric parameters Correct output on the first iteration, Number of errors embedded in the code, Number of trials required to achieve the code and Failure to generate the code after three iterations, are used to compare the efficiency of using LLM in programming. In all the cases, the generated code had significant errors and human intervention was always required to fix the bugs. LLM can therefore be used to complement a programmer code design. |
| title | Using LLM such as ChatGPT for Designing and Implementing a RISC Processor: Execution,Challenges and Limitations |
| topic | Machine Learning Hardware Architecture Software Engineering |
| url | https://arxiv.org/abs/2401.10364 |