Using LLM such as ChatGPT for Designing and Implementing a RISC
  Processor: Execution,Challenges and Limitations
Shadeeb Hossain Aayush Gohil Yizhou Wang
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.