HDLCopilot: Natural Language Exploration of Hardware Designs and Libraries

View a PDF of the paper titled HDLCopilot: Natural Language Exploration of Hardware Designs and Libraries, by Manar Abdelatty and 2 other authors

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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.