Towards LLM-Powered Verilog RTL Assistant: Self-Verification and Self-Correction

Hardware description languages like Verilog are fundamental in digital circuit design, but manual RTL coding is labor-intensive and prone to errors. Existing EDA tools have improved automation but struggle with complex timing and physical constraints. Recent research explores using LLMs for code generation, with models like GPT-4 and CodeGen applied to generate Verilog code. However, these approaches often produce low-accuracy results, especially in meeting timing and physical constraints, due to single-pass inference and lack of iterative verification. Prior works mainly focus on fine-tuning or prompt engineering without fully exploiting multi-turn reasoning or feedback mechanisms. This limits their effectiveness in real-world hardware design, which demands high correctness and reliability. The evolution of LLMs' reasoning capabilities suggests that multi-turn, iterative approaches could bridge this gap, enabling more accurate and robust RTL generation.

Current LLM-based RTL code generation methods face significant challenges: low syntactic and semantic accuracy, inability to reliably meet timing constraints, and lack of integrated verification. Single-pass inference often results in code with errors that require extensive manual debugging, hindering practical deployment. Moreover, existing systems do not incorporate feedback from RTL simulation, which is crucial for verifying timing and functional correctness. These limitations restrict the adoption of AI-assisted automation in hardware design, especially for complex or safety-critical systems. Addressing these issues requires a framework that can iteratively generate, verify, and refine RTL code, mimicking human design workflows and leveraging the reasoning power of advanced LLMs.

This work introduces a multi-turn, chain-of-thought framework that integrates automatic prompt generation, RTL simulation feedback, and iterative code refinement. The key innovations include: 1) mimicking human design processes through step-by-step reasoning; 2) leveraging RTL simulation results to identify and correct errors automatically; 3) enabling multiple iterations of code generation and verification to improve correctness. Unlike prior single-pass methods, this approach dynamically guides the LLM to reason about timing, physical constraints, and logical correctness, resulting in higher-quality RTL outputs. It also formalizes a feedback loop that effectively combines AI reasoning with hardware simulation, setting a new paradigm for AI-assisted hardware automation.

• �� Input: design task description plus a system prompt defining the model role as a Verilog designer;• Step 1: The model interprets the prompt, understands design requirements, and devises a step-by-step plan;• Step 2: The model generates RTL code based on the plan, employing chain-of-thought reasoning;• Step 3: The generated code is compiled and checked for syntax errors;• Step 4: If errors occur, RTL simulation feedback is used to identify issues, and the model is prompted to revise the code;• Step 5: Once syntax is correct, the code is tested for functionality using generated or existing test benches;• Step 6: If functional errors are found, the model walks through the code with test cases, reasons about failures, and revises accordingly;• Step 7: The process repeats until the design passes all checks or time runs out, then outputs the final RTL code and test benches.

The framework was evaluated on benchmark suites including OpenCores and ISCAS, comparing pass@5, syntax correctness, and functionality accuracy. Hyperparameters such as iteration limits and prompt design were tuned for optimal performance. Ablation studies confirmed the importance of multi-turn reasoning and feedback integration. Results showed a 10.4% increase in functionality pass rate over baseline single-pass models, with the average pass@5 reaching 72.3%. The robustness was tested across simple and complex designs, demonstrating consistent improvements. The experiments validated that iterative self-verification significantly enhances RTL code quality, especially in meeting timing constraints and physical design requirements.

VeriAssist achieved a 72.3% pass@5 rate, surpassing previous methods by 10.4%. It effectively reduces syntax errors and functional bugs, producing RTL code that meets timing and physical constraints. The iterative process enables the model to reason through complex logic, resulting in designs comparable or superior to human reference code. The automatic generation of test benches and walk-throughs further improves verification efficiency. The results demonstrate the potential of combining multi-turn reasoning with feedback-driven correction for reliable hardware automation, especially for novice users and rapid prototyping.

This approach can be integrated into EDA tools for automatic RTL code generation, verification, and debugging, reducing reliance on expert knowledge. It is suitable for educational platforms, enabling students to learn hardware design interactively. Industry applications include rapid prototyping, verification of complex designs, and safety-critical system development. The system's ability to handle timing and physical constraints makes it valuable for ASIC and FPGA design workflows, accelerating time-to-market and reducing costs.

The reliance on RTL simulation feedback introduces dependency on simulation accuracy and speed, which may limit scalability for very large designs. Computational costs increase with multiple iterations, impacting practical deployment. The framework may struggle with novel architectures outside the training distribution or highly complex physical constraints. Further research is needed to improve simulation efficiency, extend support to other HDL languages, and incorporate multi-objective optimization to handle diverse design goals.