Summary of Using Llm Such As Chatgpt For Designing and Implementing a Risc Processor: Execution,challenges and Limitations, by Shadeeb Hossain et al.
Using LLM such as ChatGPT for Designing and Implementing a RISC Processor: Execution,Challenges and Limitations
by Shadeeb Hossain, Aayush Gohil, Yizhou Wang
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Hardware Architecture (cs.AR); Software Engineering (cs.SE)
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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 explores the potential of Large Language Models (LLMs) for generating code with a focus on designing a Reduced Instruction Set Computer (RISC). The authors outline the steps involved, including parsing, tokenization, encoding, attention mechanisms, and sampling tokens. They then verify the generated code through testbenches and hardware implementation on a Field-Programmable Gate Array (FPGA) board. Four metrics are used to evaluate the efficiency of LLMs in programming: correct output on the first iteration, number of errors embedded in the code, number of trials required, and failure rate. Surprisingly, the generated code often contained significant errors, requiring human intervention to fix bugs. The study suggests that LLMs can be useful for complementing programmer-designed code. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at using big language models (LLMs) to help write computer code. They want to see if these models are good at designing a special kind of computer chip called a RISC. To do this, they walk through the steps the model would take: breaking down words into smaller parts, making sense of what it means, and choosing which words to use next. Then, they test how well the generated code works on real hardware. They compare different ways of using LLMs and find that the code often has mistakes that need human help to fix. This shows that LLMs can be helpful for creating code, but still need some human input. |
Keywords
* Artificial intelligence * Attention * Parsing * Tokenization
