RTLLM Benchmark for RTL Code Generation

• RTLLM benchmark is a structured evaluation platform that measures large language models’ ability to generate synthesizable and functionally correct RTL code for digital hardware tasks.
• It employs a triplet evaluation approach with natural language specifications, testbenches, and human-crafted reference designs to ensure consistent assessments via synthesis, simulation, and PPA metrics.
• The framework integrates self-planning prompt engineering to enhance design quality and enable fair, reproducible comparisons for agile hardware design automation.

The RTLLM benchmark is an open-source evaluation suite developed to facilitate rigorous and fair assessment of LLMs on the task of design RTL (Register-Transfer Level) code generation from natural language instructions (Lu et al., 2023). RTLLM targets digital hardware design automation, providing a unified platform that measures not only syntactic correctness and functional accuracy but also design quality metrics relevant to downstream electronic design automation (EDA) workflows. Its structure enables standardized comparisons among diverse LLM solutions in hardware description language (HDL) domains, notably Verilog, VHDL, and Chisel.

1. Benchmark Structure and Dataset Composition

RTLLM comprises a curated set of 30 digital design tasks encompassing both arithmetic (e.g., adders, multipliers, dividers) and logic/control circuits (e.g., counters, finite state machines [FSMs], traffic lights, and a simplified RISC CPU). For each task, the benchmark provides:

• Natural language design specification (𝓛): A file detailing explicit functional requirements and module I/O signals.
• Associated testbench (𝓣): Multiple test cases enabling automated simulation-based functional checking.
• Reference human-crafted RTL design (𝓥_H): Serves as a ground-truth baseline for quality evaluation.

This triplet structure ensures that LLM outputs can be systematically evaluated at various stages—compilation, simulation, and physical synthesis.

2. Progressive Evaluation Goals

RTLLM operationalizes evaluation through three progressive goals:

Goal	Check Method	Success Criterion
Syntax Goal	Synthesis tool (e.g., Design Compiler)	Code must compile with no syntax errors
Functionality	Simulation with testbench (𝓣)	Outputs must match reference for all test cases
Design Quality	Post-synthesis PPA metrics	Area, power, and timing compared to 𝓥_H

• Syntax Goal: All generated HDL must be directly synthesizable for consideration in further evaluation.
• Functionality Goal: Code must exhibit correct behavior, validated through automated simulations against comprehensive testbenches. While passing all supplied cases confirms substantial correctness, full functional coverage in arbitrary scenarios cannot be established by this test alone.
• Design Quality Goal: Designs are further evaluated through post-synthesis extraction of physical metrics critical for deployment—area (μm²), power consumption (μW), and timing (worst negative slack, WNS in ns). These are compared to reference solutions to measure real-world implementation efficiency.

3. Benchmarking Workflow and Quantitative Metrics

For each design, a fixed number of code samples (typically five) are generated per LLM. Automated scripts orchestrate the evaluation pipeline:

• Syntax Evaluation: Succinctly binary (pass/fail); number of successful synthesizable samples is recorded.
• Functionality Evaluation: Among those passing syntax, at least one passing testbench simulation yields functional pass.
• PPA Measurement: PPA metrics—area, power, timing—are extracted for all designs passing both prior checks.

The benchmark employs tabular reporting with color-coded metrics to highlight best and worst performing outputs across designs. Aggregate statistics enable cross-model performance comparisons. For modeling the code generation process, RTLLM adopts the formulation $\mathcal{V} = F(\mathcal{L}_P)$, where $\mathcal{L}_P$ is a prompt-engineered, self-planned version of the NL description, and $F$ denotes the LLM.

4. Self-Planning Prompt Engineering

One distinctive contribution of RTLLM is the "self-planning" prompt engineering technique. Instead of directly requesting code generation, the process is decomposed:

• Planning Step: LLM receives 𝓛 and is tasked with producing natural language reasoning steps and syntax advice—highlighting critical design decisions and potential syntax pitfalls (e.g., variable declarations, assignment types).
• Generation Step: The planning output, containing structured logic and error avoidance strategies, is appended to the initial prompt and submitted for actual code generation.

This method significantly improves both syntactic and functional correctness—empirically shown to elevate GPT-3.5’s performance to levels approaching GPT-4.

5. Comparison with Prior Benchmarks and Fairness

RTLLM was explicitly designed to overcome limitations in existing benchmarks:

• Design Complexity: Previous benchmarks (e.g., Chip-Chat, Chip-GPT) focused on isolated, small-scale modules tailored by authors, limiting design diversity and evaluation scope. RTLLM expands both scale and complexity, introducing larger modules and more intricate logic.
• Standardized Inputs and Outputs: Uniform natural language specifications and shared testbenches eliminate author bias and ensure that all models are evaluated on precisely the same requirements.
• Comprehensive Metrics: RTLLM extends beyond basic correctness to include PPA comparisons, yielding a more holistic measure of hardware design merit.

6. Applications, Implications, and Extensions

The benchmark's structured evaluation paradigm makes it a valuable resource for several purposes:

• Agile Hardware Design: LLMs can be integrated into EDA flows for rapid prototype generation, reducing coding overhead.
• Educational Utility: RTLLM serves as a pedagogical tool for translating natural language requirements into synthesizable HDL code.
• Comparative Tooling for LLM Solutions: It supports standardized comparisons, enabling the measurement of advances from fine-tuned, domain-specialized, or prompt-engineered models.
• Research in Prompt Strategies: Self-planning and related techniques may generalize to other program synthesis domains where multi-level reasoning is relevant.
• Potential for Extension: The RTLLM design enables future integration into iterative EDA workflows, bridging upstream code generation with downstream optimization and physical synthesis naturally.

7. Limitations and Future Directions

While RTLLM is an effective platform, certain limitations persist:

• Functional coverage: Testbench simulation provides strong correctness guarantees but cannot exhaustively test all possible logic paths.
• Design quality granularity: PPA metrics depend on synthesis tool settings and may not reflect system-level trade-offs or microarchitectural subtleties.
• Prompt sensitivity: Evaluation can be influenced by prompt wording; the self-planning methodology mitigates prompt fragility but does not fully eliminate it.
• Scale and diversity: Although RTLLM introduces higher-difficulty tasks than past benchmarks, future versions may need to further expand the design space toward very large-scale SoCs, memory systems, and advanced protocol logic.

A plausible implication is that as LLMs progress, extension of RTLLM to broader HDLs, more complex parameterized designs, and finer-grained physical evaluation will become necessary to reflect the advancing capabilities and real-world integration of automated hardware design systems.

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In summary, the RTLLM benchmark provides a systematic, quantitative framework for evaluating LLM-driven RTL code generation, combining stringent syntax and functionality assessment with domain-relevant hardware metrics and offering a reproducible means of measuring design automation advances (Lu et al., 2023). Its structured format and pioneering self-planning prompt engineering represent foundational contributions for both academic and industrial electronic design automation research.