Benchmarking Open-Source LLMs on Verilog RTL Generation Across 50 Tasks
A collaborative research team from NMIMS, IIT Roorkee, and BITS Pilani has conducted a comprehensive benchmarking study evaluating the performance of open-source large language models (LLMs) on Verilog Register-Transfer Level (RTL) code generation. The study spans 50 distinct tasks, designed to assess the models' capabilities in hardware design automation—a domain critical for semiconductor innovation as of 2026.
Key Findings
- Task Diversity and Complexity: The 50 tasks cover a wide range of RTL design scenarios, from basic combinational logic to complex sequential circuits. This breadth allows for a nuanced understanding of each model's strengths and weaknesses.
- Model Performance: Among the evaluated open-source LLMs, those fine-tuned specifically for hardware description languages showed significantly higher accuracy and syntax correctness compared to general-purpose models. However, even the best-performing models struggled with tasks requiring deep hierarchical reasoning or optimization for area and power—key concerns in modern chip design (2026 context: with the rise of AI accelerators and edge computing, power-efficient RTL generation is increasingly vital).
- Benchmarking Methodology: The team employed a rigorous evaluation framework, using metrics such as functional correctness, compile success rate, and simulation pass rate. Each task was run multiple times to account for stochastic outputs, and results were averaged for reliability.
- Open-Source Advantage: The study highlights that open-source LLMs, while not yet matching proprietary counterparts in raw performance, offer substantial transparency and customizability. This is particularly valuable for hardware teams that need to audit or adapt models for specific design flows—a growing trend in 2026 as regulatory pressure for AI explainability intensifies.
Implications for 2026 Hardware Design
- Accelerating Design Cycles: The integration of LLM-assisted RTL generation can reduce time-to-market for new chips, but current open-source models require human-in-the-loop verification to avoid bugs. The benchmarking results provide a baseline for future improvements.
- Educational and Research Impact: By publicly releasing the benchmark tasks and evaluation scripts, the team aims to foster further research. This aligns with 2026's push for open-source hardware tools and collaborative AI development in semiconductor engineering.
- Path Forward: The study identifies key areas for improvement, including better handling of design constraints, improved memory of large-scale datapath logic, and integration with formal verification tools. Future work may explore hybrid approaches combining LLMs with classical synthesis algorithms.
Conclusion
This benchmarking effort represents a significant step toward evaluating and enhancing open-source LLMs for Verilog RTL generation. As AI continues to permeate hardware design in 2026, such studies are essential for bridging the gap between general-purpose language models and the specialized demands of semiconductor engineering. The findings underscore both the promise and the current limitations of open-source LLMs in this high-stakes domain.