VToT: Automatic Verilog Generation via LLMs with Tree of Thoughts Prompting
Yingjie Zhou, Renzhi Chen, Xinyu Li, Jingkai Wang, Zhigang Fang, Bowei Wang, Wenqiang Bai, Qilin Cao, Lei Wang · 2025
The automatic generation of Verilog code using Large Language Models (LLMs) presents a compelling solution to enhance the efficiency of hardware design flow. However, the state-of-the-art performance of LLMs in Verilog generation remains limited compared to programming languages such as Python. Previous research, Chain of Thought (CoT), has demonstrated that incorporating intermediate reasoning steps can significantly improve the performance of LLMs in code generation. In this paper, we propose the Verilog Tree of Thoughts (VToT) method. This structured prompting technique addresses the abstraction gap between Verilog and CoT by embedding hierarchical design constraints within the prompt. Experimental results on the VerilogEval and RTLLM benchmarks demonstrate that VToT prompting enhances both the syntactic and functional correctness of the generated code. Specifically, according to the RTLLM benchmark, VToT achieved a correctness rate of 75.9% at pass@5, representing an improvement of 10.4%. Furthermore, in the VerilogEval benchmark, VToT achieved state-of-the-art performance with a correctness rate of 52.4% at pass@1 (an increase of 8.9%) and 65.4% at pass@5 (an increase of 9.6%).