VerilogMonkey: Exploring Parallel Scaling for Automated Verilog Code Generation with LLMs

2025-09-17

AI Summary

This study investigates the impact of parallel scaling on large language models’ (LLMs) performance in automated Verilog code generation. Addressing the limited functional correctness of LLM-generated hardware designs—attributed to output randomness and single-sample decoding bias—we propose a lightweight, fine-tuning-free, post-training-free parallel sampling method that generates multiple candidate solutions concurrently. We systematically evaluate this approach across mainstream open-weight LLMs (e.g., Llama-3, Qwen2) and established benchmarks (VerilogEval, HDLBits), demonstrating a strong positive correlation between parallel scale (up to hundreds of samples) and generation quality. Crucially, we identify and empirically validate the mechanism by which statistical aggregation over parallel samples suppresses stochasticity and improves functional correctness. Experiments show that our method achieves substantial gains under controllable latency and computational cost, outperforming state-of-the-art LLM-based Verilog generators—with up to a 27.4% absolute improvement in functional correctness.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Search and Optimization: Sampling/Simulation-based Search

Application Category

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

Abstract

We present VerilogMonkey, an empirical study of parallel scaling for the under-explored task of automated Verilog generation. Parallel scaling improves LLM performance by sampling many outputs in parallel. Across multiple benchmarks and mainstream LLMs, we find that scaling to hundreds of samples is cost-effective in both time and money and, even without any additional enhancements such as post-training or agentic methods, surpasses prior results on LLM-based Verilog generation. We further dissect why parallel scaling delivers these gains and show how output randomness in LLMs affects its effectiveness.

Problem

• Exploring parallel scaling for automated Verilog code generation
• Improving LLM performance through parallel output sampling
• Analyzing how output randomness affects Verilog generation effectiveness

Innovation

• Parallel scaling samples many LLM outputs
• Cost-effective scaling to hundreds of samples
• Analyzes output randomness impact on effectiveness