LLM4RTL: Tool-Assisted LLM for RTL Generation

The article introduces LLM4RTL, a tool-assisted architecture designed for generating Register Transfer Level (RTL) code from functional descriptions. It employs a "judge-renew-check-renew-check" (JRCRC) pipeline to enhance a public dataset by filtering and refining code samples, achieving notable improvements in the VerilogEval benchmark. This approach addresses common weaknesses in LLMs regarding rule-based reasoning and logic, allowing practitioners to utilize a smaller model with performance on par with larger systems like GPT-4O, thus providing a cost-effective solution for RTL generation in hardware design.