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This paper discusses the feasibility of using Large Language Models LLM forcode generation with a particular application in designing an RISC. The paperalso 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 andhardware implementation on a FPGA board. Four metric parameters Correct outputon the first iteration, Number of errors embedded in the code, Number of trialsrequired to achieve the code and Failure to generate the code after threeiterations, are used to compare the efficiency of using LLM in programming. Inall the cases, the generated code had significant errors and human interventionwas always required to fix the bugs. LLM can therefore be used to complement aprogrammer code design.

Using LLM such as ChatGPT for Designing and Implementing a RISC Processor: Execution,Challenges and Limitations