ARAICLDCMAAug 21, 2025

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

arXiv:2508.15940v11 citationsh-index: 1Has Code2025 IEEE International Conference on LLM-Aided Design (ICLAD)
Originality Incremental advance
AI Analysis

This addresses the problem of inefficiency in ASIC design workflows for hardware engineers, though it appears incremental as it builds on existing LLM capabilities with a specialized architecture.

The paper tackles the limitations of LLMs in real-world hardware design by introducing ASIC-Agent, an autonomous multi-agent system for ASIC design, which successfully automates a range of tasks and accelerates the workflow when powered by Claude 4 Sonnet.

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.

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