Existing large language models (LLMs) for register transfer level code
generation face challenges like compilation failures and suboptimal power,
performance, and area (PPA) efficiency. This is due to the lack of PPA
awareness in conventional transformer decoding algorithms. In response, we
present an automated transformer decoding algorithm that integrates Monte Carlo
tree-search for lookahead, guiding the transformer to produce compilable,
functionally correct, and PPA-optimized code. Empirical evaluation with a
fine-tuned language model on RTL codesets shows that our proposed technique
consistently generates functionally correct code compared to prompting-only
methods and effectively addresses the PPA-unawareness drawback of naive large
language models. For the largest design generated by the state-of-the-art LLM
(16-bit adder), our technique can achieve a 31.8% improvement in the area-delay
product.

通过引入蒙特卡罗树搜索，并将其与自动转换器解码算法相结合，以解决现有大型语言模型在寄存器传输级代码生成中遇到的问题，从而实现能够生成可编译、功能正确且功耗性能区域（PPA）优化的代码。经实证评估，我们的技术在 RTL 代码集上通过对细调的语言模型进行评估，相对于仅使用提示的方法，一直能够生成功能正确的代码，并有效解决了朴素大型语言模型的 PPA 非感知缺陷。对于由最先进的大型语言模型（16 位加法器）生成的最大设计，我们的技术可以在延迟面积乘积方面实现 31.8% 的改进。