标题： DecoRTL：一种基于大语言模型的RTL代码生成运行时解码框架 显示英文标题

标题： DecoRTL: A Run-time Decoding Framework for RTL Code Generation with LLMs

摘要： 作为其众多应用之一，大型语言模型（LLMs）最近在自动化寄存器传输级（RTL）代码生成方面显示出前景。 然而，传统的LLM解码策略最初是为自然语言设计的，通常无法满足RTL的结构和语义需求，导致产生幻觉、重复或无效的代码输出。 在本文中，我们首先通过在RTL生成过程中对令牌级别的熵进行实证分析，研究这些解码失败的根本原因。 我们的研究结果表明，LLMs在结构模糊或语义复杂的区域表现出低置信度，这表明标准的解码策略无法区分需要确定性（语法关键区域）的区域和那些从创造性探索性变化中受益的区域（设计关键区域）。 然后，为了解决这个问题，我们引入了DecoRTL，一种新的运行时解码策略，该策略在RTL代码生成中既具有语法感知性又具有对比性。 DecoRTL集成了两个互补的组件：（i）自一致性采样，它生成多个候选并根据令牌级别的协议重新排序，以促进正确性同时保持多样性；以及（ii）语法感知温度适应，它根据令牌的语法和功能角色对它们进行分类，并相应地调整采样温度，对语法关键令牌施加低温度，对探索性令牌施加更高温度。 我们的方法完全在推理时运行，无需任何额外的模型微调。 通过使用VerilogEval基准在多个开源LLMs上进行评估，我们展示了在语法有效性、功能正确性和输出多样性方面的显著改进，而执行开销（性能开销）是难以察觉的。 显示英文摘要

摘要： As one of their many applications, large language models (LLMs) have recently shown promise in automating register transfer level (RTL) code generation. However, conventional LLM decoding strategies, originally designed for natural language, often fail to meet the structural and semantic demands of RTL, leading to hallucinated, repetitive, or invalid code outputs. In this paper, we first investigate the root causes of these decoding failures through an empirical analysis of token-level entropy during RTL generation. Our findings reveal that LLMs exhibit low confidence in regions of structural ambiguity or semantic complexity, showing that standard decoding strategies fail to differentiate between regions requiring determinism (syntax-critical regions) and those that benefit from creative exploratory variability (design-critical regions). Then, to overcome this, we introduce DecoRTL, a novel run-time decoding strategy, that is both syntax-aware and contrastive for RTL code generation. DecoRTL integrates two complementary components: (i) self-consistency sampling, which generates multiple candidates and re-ranks them based on token-level agreement to promote correctness while maintaining diversity; and (ii) syntax-aware temperature adaptation, which classifies tokens by their syntactical and functional roles and adjusts the sampling temperature accordingly, enforcing low temperature for syntax-critical tokens and higher temperature for exploratory ones. Our approach operates entirely at inference time without requiring any additional model fine-tuning. Through evaluations on multiple open-source LLMs using the VerilogEval benchmark, we demonstrate significant improvements in syntactic validity, functional correctness, and output diversity, while the execution overhead (performance overhead) is imperceptible.