AutoBench: Automatic Testbench Generation and Evaluation Using LLMs for HDL Design (2407.03891v2)

Abstract: In digital circuit design, testbenches constitute the cornerstone of simulation-based hardware verification. Traditional methodologies for testbench generation during simulation-based hardware verification still remain partially manual, resulting in inefficiencies in testing various scenarios and requiring expensive time from designers. LLMs have demonstrated their potential in automating the circuit design flow. However, directly applying LLMs to generate testbenches suffers from a low pass rate. To address this challenge, we introduce AutoBench, the first LLM-based testbench generator for digital circuit design, which requires only the description of the design under test (DUT) to automatically generate comprehensive testbenches. In AutoBench, a hybrid testbench structure and a self-checking system are realized using LLMs. To validate the generated testbenches, we also introduce an automated testbench evaluation framework to evaluate the quality of generated testbenches from multiple perspectives. Experimental results demonstrate that AutoBench achieves a 57% improvement in the testbench pass@1 ratio compared with the baseline that directly generates testbenches using LLMs. For 75 sequential circuits, AutoBench successfully has a 3.36 times testbench pass@1 ratio compared with the baseline. The source codes and experimental results are open-sourced at this link: https://github.com/AutoBench/AutoBench

• The paper introduces AutoBench, a novel framework that automates testbench generation for HDL design using large language models and hybrid code synthesis.
• It integrates Python and Verilog code to incrementally generate and debug testbenches, significantly improving syntax accuracy and pass@1 ratios.
• The evaluation framework AutoEval applies multiple metrics to ensure high-quality, robust testbenches, streamlining hardware verification processes.

Automatic Testbench Generation and Evaluation Using LLMs for HDL Design

The paper "AutoBench: Automatic Testbench Generation and Evaluation Using LLMs for HDL Design" introduces a novel framework aimed at automating testbench generation for digital circuit design using LLMs. This research addresses inefficiencies in traditional methods of creating testbenches for Hardware Description Language (HDL) that are largely manual and resource-intensive. The essence of this work lies in leveraging LLMs to not only generate testbenches but also evaluate their effectiveness through a structured and automated evaluation framework.

Summary of Contributions

The contributions of this paper are manifold, as encapsulated below:

1. Introduction of AutoBench: The authors present AutoBench, a pioneering LLM-based framework that automatically generates Verilog testbenches using only the description of the design under test (DUT). This approach aims to streamline the otherwise manual and laborious process of creating testbenches.
2. Hybrid Testbench Architecture: AutoBench employs a hybrid testbench architecture that integrates LLM-generated Python and Verilog code. This architecture minimizes potential conflicts that can arise when using the same source to verify RTL code and also leverages the strengths of Python for ease of coding and debugging.
3. Comprehensive LLM-Based Code Generation: The framework includes stages such as scenario generation, hybrid code synthesis, scenario checking, and automatic code debugging, enhancing the overall robustness and coverage of the generated testbenches.
4. Automated Evaluation Framework (AutoEval): To assess the quality of generated testbenches, the authors introduce AutoEval, which applies multiple metrics to evaluate the effectiveness and coverage of the testbenches. This dual role of generation and evaluation ensures a high standard of verification for generated testbenches.

Numerical Results and Key Findings

The experimental results demonstrate significant improvements in the quality and coverage of testbenches generated by AutoBench. When compared to a baseline approach where LLMs directly generate testbenches, AutoBench shows a notable increase in the pass@1 ratio under the Eval2 criterion. Specifically:

• General Improvements: AutoBench exhibits a 57% improvement in the Eval2 pass@1 ratio, underscoring its enhanced capability in generating high-quality testbenches.
• Sequential Circuits: For sequential circuits, AutoBench achieves a remarkable 3.36 times improvement in the Eval2 pass@1 ratio compared to the baseline. This highlights the framework's proficiency in handling more complex circuit designs.
• Syntax Accuracy: The pass@1 ratio in Eval0, which checks for syntactical correctness, is significantly higher (97.33% versus 55.47% in the baseline) for sequential circuits due to the self-improvement techniques integrated into AutoBench.

Methodological Innovations

The methodology adopted in AutoBench is meticulously designed to overcome the challenges associated with LLM laziness and hallucination, which detract from the reliability of directly generated testbenches. Key stages of the workflow include:

• Circuit Type Discrimination: The framework initially identifies whether the DUT is combinational or sequential. This classification tailors subsequent stages of generation to the specific needs of the circuit type.
• Scenario Generation: By dividing the testbench generation into phases, starting with the generation of detailed test scenarios, AutoBench ensures a comprehensive range of test inputs is covered.
• Incremental Code Synthesis: The framework incrementally generates the testbench code, separating the driver and checker designs. This step-by-step approach improves coverage and accuracy.
• Self-Enhancement: AutoBench includes self-enhancement modules like code standardization, scenario checking, and auto-debugging to refine and validate the testbenches iteratively, further reducing syntactical and functional errors.

Theoretical and Practical Implications

Theoretically, the framework validates the efficacy of LLMs in complex technical domains such as hardware verification. The authors extend the application of LLMs beyond traditional software contexts, pushing the envelope in electronic design automation (EDA).

Practically, AutoBench holds the promise of significantly reducing the time and cost associated with testbench generation. Automating this process can lead to faster verification cycles and reduced human intervention, which is particularly beneficial in large-scale integrated circuit (IC) and application-specific integrated circuit (ASIC) projects. The ability to maintain high coverage and accuracy also ensures more reliable hardware designs.

Future Directions

Given the promising results, several avenues for future research and development emerge:

• Enhanced Scenario Generation: Future work could explore more sophisticated techniques for scenario generation, potentially integrating ML methods to predict and create high-coverage scenarios.
• Broader Application to Different LLMs: Evaluating the framework's performance with different LLMs and hybrid models could lead to further refinements and better generalization across various hardware designs.
• Integration with Formal Verification: Combining AutoBench with formal verification methods could provide a more holistic verification framework that leverages the strengths of both simulation-based and formal methods.
• Expanded Dataset Utilization: Using a more extensive and diverse dataset could improve the robustness and effectiveness of the framework, especially for corner cases in hardware verification.

Conclusion

"AutoBench: Automatic Testbench Generation and Evaluation Using LLMs for HDL Design" presents a significant step forward in automating the hardware verification process. By systematically employing LLMs to generate and evaluate testbenches, the authors address a critical bottleneck in digital circuit design workflows. The results showcase the potential of this framework to enhance verification quality, reduce design time, and foster greater efficiency in the hardware design process. This foundational work paves the way for further research and practical advancements in the automation of HDL design and verification.