Spec2RTL-Agent: Automated RTL Code Generation

• Spec2RTL-Agent is a multi-agent system that automates the translation of complex hardware specifications into functionally correct, synthesizable RTL-level code by decomposing the task into specialized sub-functions.
• It integrates iterative reasoning, progressive coding, and adaptive reflection modules to generate validated C++ code for HLS conversion, ensuring robust hardware design automation.
• The system achieves 100% functional correctness with 75% fewer human interventions, demonstrating significant performance improvements over single-shot LLM approaches.

Spec2RTL-Agent is a multi-agent system that automates the translation of complex hardware specification documents into functionally correct and synthesizable Register Transfer Level (RTL) code, with minimal human intervention. Distinguished from previous single-shot LLM approaches, Spec2RTL-Agent employs structured collaboration between specialized agents—spanning specification decomposition, progressive coding across multiple representations, iterative verification, and adaptive error reflection—to create a robust pipeline for hardware design automation. Its architecture explicitly addresses the practical demands of real-world RTL implementation, surpassing prior systems that operate only on simplified descriptions or depend on extensive manual curation (Yu et al., 16 Jun 2025).

1. Multi-Agent Framework and Role Composition

Spec2RTL-Agent organizes the spec-to-RTL pipeline into three interconnected collaborative modules, each realized through a set of specialized agents (Yu et al., 16 Jun 2025):

• Iterative Reasoning & Understanding Module: This module ingests unstructured hardware specifications (PDFs, tables, equations) and iteratively decomposes the top-level design into a sequence of sub-functions with rich “info dicts” suitable for downstream code synthesis. The decomposition flow includes a Summarization Agent (section chunking), Decomposer Agent (sub-function identification), and Description Agent–Verifier Agent interactions to capture implementation details and constraints for each functional block.
• Progressive Coding & Prompt Optimization Module: The agents move step-wise through pseudocode, Python, and synthesizable C++ generation for each sub-function. After each intermediate code artifact, a Verifier Agent applies tests derived from the specification or previous stages. Prompt optimization is guided by a dedicated PromptOptimizer Agent, which revises prompts based on systematic error detection from coder–verifier logs, minimizing retries and promoting synthesisability.
• Adaptive Reflection Module: Errors surfacing during verification are traced back across all log data by an Analysis Agent, which hypothesizes root causes and error propagation paths. The Reflection Agent then decides whether to revisit the original decomposition, patch previous sub-functions, retry current code generation, or escalate to human intervention. This ensures the source and context of errors are addressed efficiently.

A CodeOptimizer Agent applies final HLS-specific conventions, and synthesis is performed via Cadence Stratus HLS.

2. Operational Workflow and Algorithmic Formalism

The workflow is mathematically structured through notation for plan state $P_t$, prompt $p_i$, coding attempt $code_i^{(t)}$, and error-tracing function $E(\cdot)$, as follows (Yu et al., 16 Jun 2025):

Module 1 – Reasoning & Decomposition

Summaries ← SummarizationAgent.chunk_and_summarize(S) Decomposition ← DecomposerAgent.decompose(Summaries, S) for each subfunc fᵢ: infoᵢ ← DescriptionAgent.extract(S, Summaries, fᵢ) while not VerifierAgent.approve(infoᵢ): infoᵢ ← DescriptionAgent.refine(infoᵢ, VerifierAgent.feedback()) return P₀ = {(fᵢ, infoᵢ)}

Module 2 – Progressive Coding

for i=1 to n: for t=0,1,2,… until pass: codeᵢ^(t) ← CoderAgent.generate(infoᵢ, pᵢ^(t)) pass, error_log ← VerifierAgent.test(codeᵢ^(t)) if pass then break pᵢ^(t+1) ← PromptOptimizerAgent.improve(pᵢ^(t), error_log) C ← integrate(C, codeᵢ^(t)) return C

The prompt is optimized to minimize the expected number of retries:

$$p_i^{(t+1)} = \operatorname{argmin}_{p'} E[\#\text{retries} | \text{Coder}(p'), \text{Verifier}]$$

Module 3 – Adaptive Reflection

candidates ← AnalysisAgent.hypothesize(L, T) action ← ReflectionAgent.select(candidates) switch action: case “revise_plan”: go to Module 1 case “fix_prev_funcs”: for each fⱼ in candidates.prev_functions: treat as new sub-function case “retry_current”: re-enter Module 2 case “escalate”: prompt human with L and T

Error tracing leverages test vector provenance and mismatch quantification per code block.

3. C++/HLS-Centric Code Generation Strategy

Spec2RTL-Agent exclusively generates synthesizable C++ code for HLS conversion, rather than direct RTL emission. The rationale is as follows (Yu et al., 16 Jun 2025):

• LLMs demonstrate greater reliability and correctness at abstract, algorithmic code generation (pseudocode, Python, C++) as opposed to cycle-level Verilog, which often results in syntax or synthesis failures.
• By targeting HLS flows, strict scheduling, resource allocation, and hardware constraints are enforced, including fixed-width integer data types, static allocation, and non-recursive structures.
• Synthesis metrics from Cadence Stratus HLS (latency, area, throughput) are directly obtained, and design outputs (e.g., AES cores) match hand-tuned open-source implementations.
• Direct Verilog generation by LLMs typically results in non-synthesizable constructs and corner-case functional failures, requiring ad hoc manual repairs.

4. Evaluation Methodology and Quantitative Performance

Spec2RTL-Agent was benchmarked on three FIPS specification documents—AES (FIPS-197), DSS (FIPS-186-5), and HMAC (FIPS-198-1)—and compared against multiple baselines (Yu et al., 16 Jun 2025):

Method	#Correct	#Interventions	#Code Attempts
Human	3/3	≈20	≈20
Single-Shot	0/3	–	–
W/o Understand	2/3	18.7	15.5
NaiveCoding	3/3	9.0	17.5
W/o Reflection	3/3	6.3	13.2
Spec2RTL-Agent	3/3	4.3	9.1

• Spec2RTL-Agent consistently achieved end-to-end functional correctness (3/3 cases) with 75% fewer human interventions than standard methods.
• Robustness was demonstrated by sustaining performance when semantic faults were injected, with only a 10% increase in coding iterations or interventions.
• Sub-function iteration averaged ~10 attempts, reflecting substantial improvements in reduction of manual oversight compared to engineer-in-the-loop flows.

5. Error Tracing, Verification, and Prompt Optimization

The agent system incorporates adaptive reflection to isolate and correct errors at all granularity levels. Key mechanisms include:

• Mapping failing tests to corresponding plan items or sub-function implementations.
• Quantifying mismatch frequency per code instruction or block, enabling focused remediation.
• Selection among error correction pathways: revisiting decomposition, patching prior sub-functions, regenerating current implementations, or requesting human input for unresolved root causes.
• Automated revision of coder prompts by the PromptOptimizer Agent—a strategy that demonstrably reduces retries and converges to functional synthesizability.

This tightly closed feedback loop, encompassing automated decomposition, multi-representation coding, and iterative error correction, increases the robustness of the design generation flow.

6. Impact, Limitations, and Prospective Advances

Spec2RTL-Agent establishes that human-like hardware engineering workflows—spanning comprehension, decomposition, progressive code refinement, and rigorous error tracing—can be replicated with collaborative LLM agents to robustly automate hardware RTL generation (Yu et al., 16 Jun 2025). Notable impact includes dramatic reductions in human effort and reliable synthesis of complex, multi-functional hardware modules.

Limitations include:

• Average iteration counts per sub-function remain nontrivial, incurring moderate token and latency costs.
• Full autonomy is not achieved; some corner-case specifications still trigger escalation to human review.
• Preprocessing of specification documents (to handle figures and tables) is required, suggesting an avenue for multi-modal LLM integration.

Future directions highlighted include:

• Token-efficient prompting and in-model caching.
• Reinforcement learning or evolutionary techniques to accelerate agent policy learning.
• Extensions to multi-clock, asynchronous SoC scenarios.
• Integrated performance-aware PPA (power, performance, area) optimization.

These developments suggest that agent-based hardware design automation is on course for practical real-world deployment, especially as LLM capabilities and agent orchestration policies improve.