ASIC-Agent-Bench: Autonomous ASIC Design Evaluation

• ASIC-Agent-Bench is a structured benchmark evaluating full ASIC design pipelines with autonomous agents and LLM-driven assessments.
• It employs a hybrid methodology combining automated testing and LLM-based judging to quantitatively and qualitatively assess design flow performance.
• The benchmark, applied on ASIC-Agent, enhances iterative design, troubleshooting, and integration, driving systematic research in autonomous ASIC design.

ASIC-Agent-Bench is a structured benchmark specifically developed to evaluate autonomous agentic systems in the end-to-end Application-Specific Integrated Circuit (ASIC) design workflow. Its architecture reflects the complexity and multi-phase integration requirements of real-world digital hardware development, serving as a definitive validation suite for agent behavior covering design, verification, physical implementation, and tool ecosystem interaction. The benchmark has been deployed to assess ASIC-Agent, a multi-agent system for ASIC design that leverages LLMs and domain-specific knowledge bases, enabling quantitative grading and qualitative assessment across several base LLMs (Allam et al., 21 Aug 2025).

1. Benchmark Purpose and Architectural Principles

ASIC-Agent-Bench is crafted to transcend narrow code generation benchmarks by encompassing the full ASIC design pipeline. The benchmark evaluates the agent’s capacity for iterative design, multi-tool coordination, troubleshooting, and integration into established silicon design frameworks. Its scope includes:

• Full Design Flow Coverage: Tasks span RTL generation, simulation-based verification, physical OpenLane hardening, and Caravel chip integration.
• Diversity of Tasks: Modules range from isolated combinational/sequential logic blocks to integrated system-on-chip components, with varying degrees of pipeline complexity and control granularity.
• Agent Autonomy: Agents self-organize internal workflows, develop their own cocotb testbenches, invoke EDA tools, and adapt their strategies based on observed environment failures or tool feedback.

Checkpoints are established at each logical stage, which are verified via “Observable Artifacts” (code files, simulation logs, tool outputs, and configuration snapshots), ensuring the evaluation is fully automatable and binary in its assessment.

2. Evaluation Methodology and Metrics

The benchmark utilizes a hybrid evaluation mechanism anchored on both automated testing scripts and an LLM-based evaluator framework. Key methodological components include:

• LLM-Powered Judging: A fixed base model (gemini-2.5-pro) serves as the judge—parsing agent-submitted codebases, matching them against checkpoint lists, and interpreting qualitative attributes (e.g., code modularity, adherence to design conventions).
• Multi-Stage, Weighted Scoring: The final score is computed as a weighted sum of achievements over sub-tasks, per the provided LaTeX formula:

$$\text{Final Score} = \frac{4}{7} \times 15 + \frac{6}{6} \times 15 + \frac{1}{1} \times 20 + \frac{2}{2} \times 50 \approx 93\%$$

• Partial Credit Allocation: For incomplete executions or partial fulfillment of requirements (e.g., a module that passes simulation but fails hardening), partial scores are awarded reflecting design realism.
• Automated Testing: Simulation validation (e.g., shell execution of “./run_test.sh” with result code checks) and OpenLane physical flow completion (including DRC/LVS, PPA metric extraction) enforce pipeline correctness.

This rigor ensures that benchmark results map closely to practical industry expectations for agent-driven ASIC design.

3. Agent-Driven Benchmark Utilization

ASIC-Agent’s multi-agent system serves as the canonical subject for ASIC-Agent-Bench. Specialized sub-agents are assigned to logical phases:

• Main Agent: RTL code specification and initial synthesis.
• Verification Agent: Construction and deployment of cocotb testbenches, covering boundary and signal propagation conditions.
• Hardening Agent: OpenLane .tcl configuration, iterative design refinement based on tool feedback, timing/area optimization.
• Integration Agent: Handling Caravel SoC interfaces, ensuring compliance and seamless design inclusion.

This agentic decomposition is crucial for benchmarking workflow modularity, debugging prowess, adaptive planning, and knowledge base utilization (vector database for documentation, error taxonomy retrieval, etc.). Each agent’s efficacy is independently and collectively assessed at per-task checkpoints.

4. Quantitative and Qualitative Results

The paper presents empirical performance breakdowns of ASIC-Agent when powered by multiple LLMs (Claude 4 Sonnet, GPT-4.1, Gemini 2.5 Pro):

LLM Backbone	Avg. Score (%)	Steps	Cost ($)	Qualitative Behavior
Claude 4 Sonnet	88	High	Higher	Superior debugging, adaptive error recovery
GPT-4.1	60.8	Lower	Lower	More cost-effective, lower complex task coverage
Gemini 2.5 Pro	Intermediate	Mixed	Mixed	Sample-level coverage varies

For high-complexity tasks (e.g., a RISC-V processor core or neural network accelerator), agents with high-performing LLMs can achieve scores approaching 93% (as shown in the weighted breakdown), albeit with increased resource overhead and step count. This suggests a direct correlation between underlying LLM reasoning abilities and hardware design success in autonomous settings.

Qualitative observations highlight Claude 4 Sonnet’s effective resolution of lint and synthesis errors, strategic use of external vector database knowledge (for OpenLane error debugging), and iterative optimization across pipeline stages, yielding notable performance gains on complex designs.

5. Observability, Scoring, and Real-World Alignment

Evaluation checkpoints correspond to directly observable outputs:

• Interface Correctness: Signal declaration, pipeline stages, control implementation.
• Simulation Pass: Testbench comprehensiveness, exhaustive coverage.
• Physical Implementation: Success of OpenLane hardening, GDSII artifact generation, PPA metrics, and regulatory report pass/fail.

Scoring weights reflect industry priorities (e.g., physical flow success accounts for 50/100 points in exemplar tasks) and ensure that partial progress (e.g., passing simulation but failing hardening) is appropriately acknowledged. This structure guarantees that the benchmark outcome is both transparent and applicable to practical ASIC workflows.

6. Future Prospects and Benchmark Significance

ASIC-Agent-Bench establishes an objective, automatable framework for evaluating agentic systems in digital hardware design—a domain previously lacking agent-centric benchmarking standards. Its architecture supports ongoing LLM advances and multi-agent system evolution, with explicit metrics enabling comparative analyses across agent variants, algorithmic improvements, and knowledge base integrations. A plausible implication is that this methodology could inform future extensions to analog design, mixed-signal systems, and broader EDA toolchains.

By demonstrating robust performance evaluation—from granular checkpoint accounting to full pipeline assessment—ASIC-Agent-Bench paves the way for systematic, reproducible research in autonomous, scalable ASIC development.