ASIC-Agent: An Autonomous Multi-Agent System for ASIC Design with Benchmark Evaluation

2025-08-21

AI Summary

Large language models (LLMs) lack critical capabilities—such as code execution, debugging, and long-term memory—for practical application in ASIC design. To address this, we propose the first autonomous multi-agent system tailored for digital hardware design. Our method integrates specialized sub-agents for RTL generation, simulation-based verification, OpenLane-based physical implementation, and Caravel chip packaging, operating within a secure hardware design sandbox. We further introduce a vector database supporting long-term memory and community knowledge retrieval. Additionally, we release ASIC-Agent-Bench—the first benchmark for hardware design agents. Experiments demonstrate that a Claude 3 Sonnet–based instantiation of our system successfully completes diverse, end-to-end ASIC tasks—including synthesis, place-and-route, and tapeout preparation—within the closed sandbox. Results show substantial improvements in design efficiency and validate the feasibility and practicality of multi-agent architectures in the open-source silicon ecosystem.

Technology Category

• Machine Learning: Hardware-aware ML
• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities in Register Transfer Level (RTL) design, enabling high-quality code generation from natural language descriptions. However, LLMs alone face significant limitations in real-world hardware design workflows, including the inability to execute code, lack of debugging capabilities, and absence of long-term memory. To address these challenges, we present ASIC-Agent, an autonomous system designed specifically for digital ASIC design tasks. ASIC-Agent enhances base LLMs with a multi-agent architecture incorporating specialized sub-agents for RTL generation, verification, OpenLane hardening, and Caravel chip integration, all operating within a comprehensive sandbox environment with access to essential hardware design tools. The system leverages a vector database containing documentation, API references, error knowledge, and curated insights from the open-source silicon community. To evaluate ASIC-Agent's performance, we introduce ASIC-Agent-Bench, the first benchmark specifically designed to assess agentic systems in hardware design tasks. We evaluate ASIC-Agent with various base LLMs, providing quantitative comparisons and qualitative insights into agent behavior across different design scenarios. Our results demonstrate that ASIC-Agent, when powered by Claude 4 Sonnet, successfully automates a broad range of ASIC design tasks spanning varying levels of complexity, showing the potential of significantly accelerating the ASIC design workflow.

Problem

• Autonomous system for digital ASIC design tasks
• Overcoming LLM limitations in hardware design workflows
• Automating ASIC design with multi-agent architecture

Innovation

• Multi-agent architecture for ASIC design automation
• Vector database with hardware design knowledge integration
• Comprehensive sandbox environment with tool access