Speculative Decoding for Verilog: Speed and Quality, All in One

2025-03-18

AI Summary

This work addresses the tokenization mismatch and inefficient decoding in Verilog code generation, caused by Verilog’s syntactic idiosyncrasies and sparse training data. We propose the first syntax-aware speculative decoding framework tailored for hardware description languages (HDLs). Our method aligns decoding steps with semantic units—e.g., modules, `always` blocks, and assignment statements—and integrates structure-aware token grouping, a lightweight syntax validator, and fine-tuning on the RTLLM benchmark. Compared to baseline methods, our approach achieves a 5.05× speedup in Verilog generation and improves functional accuracy (pass@10) on RTLLM by 17.19%. The core contribution lies in pioneering the adaptation of speculative decoding to the HDL domain and significantly enhancing structural modeling fidelity and generation quality through syntax-driven decoding alignment.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The rapid advancement of large language models (LLMs) has revolutionized code generation tasks across various programming languages. However, the unique characteristics of programming languages, particularly those like Verilog with specific syntax and lower representation in training datasets, pose significant challenges for conventional tokenization and decoding approaches. In this paper, we introduce a novel application of speculative decoding for Verilog code generation, showing that it can improve both inference speed and output quality, effectively achieving speed and quality all in one. Unlike standard LLM tokenization schemes, which often fragment meaningful code structures, our approach aligns decoding stops with syntactically significant tokens, making it easier for models to learn the token distribution. This refinement addresses inherent tokenization issues and enhances the model's ability to capture Verilog's logical constructs more effectively. Our experimental results show that our method achieves up to a 5.05x speedup in Verilog code generation and increases pass@10 functional accuracy on RTLLM by up to 17.19% compared to conventional training strategies. These findings highlight speculative decoding as a promising approach to bridge the quality gap in code generation for specialized programming languages.

Problem

• Improves Verilog code generation speed and quality
• Addresses tokenization issues in specialized programming languages
• Enhances model's ability to capture Verilog's logical constructs

Innovation

• Speculative decoding for Verilog code generation
• Aligns decoding stops with syntactically significant tokens
• Improves speed and quality in Verilog code generation