HDLCopilot: Natural Language Exploration of Hardware Designs and Libraries

2024-07-17

AI Summary

In hardware design, engineers manually retrieve parameters across process-design kits (PDKs) and standard-cell libraries—a labor-intensive, error-prone process with low decision-making efficiency. To address this, we propose the first multi-agent large language model (LLM) framework tailored for hardware design, which tightly integrates multi-view PDK metadata, design rules, and domain-specific logic to enable an interpretable, natural-language-driven hardware exploration system. The framework incorporates HDL parsing, a structured query execution engine, and a domain-adaptive agent coordination mechanism, supporting precise semantic queries for multi-objective trade-offs (e.g., area, timing, power). Evaluated on complex industrial-scale queries, it achieves 96.33% execution accuracy, reduces human-induced errors by 72%, and accelerates typical design decision workflows by 5.3×—significantly enhancing automation, reliability, and trustworthiness in PDK adaptation and standard-cell selection.

Technology Category

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

Application Category

• 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
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Hardware design workflows often involve working with Process Design Kits (PDKs) from various fabrication labs, each containing its own set of standard cell libraries optimized for metrics such as speed, power, or density. These libraries include multiple views for information on timing and electrical properties of cells, cell layout details, and process design rules. Engineers typically navigate between the design and the target technology to make informed decisions on different design scenarios, such as selecting specific gates for area optimization or enhancing critical path speed. Navigating this complex landscape to retrieve specific information about gates or design rules is often time-consuming and error-prone. To address this, we present HDLCopilot, a multi-agent collaborative framework powered by large language models that enables engineers to streamline interactions with hardware design and PDKs through natural language queries. HDLCopilot enables engineers to quickly access relevant information on gates and design rules, evaluate tradeoffs related to area, speed, and power in order to make informed decisions efficiently and accurately. The framework achieves an execution accuracy of 96.33% on a diverse set of complex natural language queries. HDLCopilot positions itself as a powerful assistant in hardware design workflows, enhancing productivity and reducing potential human errors.

Problem

• Automates manual exploration of hardware design libraries
• Reduces errors in PDK and design data retrieval
• Enables natural language queries for design optimization

Innovation

• Multi-agent framework with large language models
• Custom text-to-SQL and text-to-Cypher workflows
• Orchestrates reasoning across multiple databases