FPGA developers often spend hours manually transcribing pin mappings from PDF schematics into constraint files. This case study explores a workflow where an AI agent processes a 12-page Zynq7010/7020 schematic, extracting over 400 pin entries into a structured, reusable knowledge base. The approach demonstrates how AI can streamline hardware design tasks, improve accuracy, and enable faster iteration. Key considerations include handling varied schematic formats, validating extracted data, and integrating the output with existing FPGA toolchains. This workflow is particularly valuable for teams working with complex boards or frequent design revisions.
A practical case study on using AI agents to extract pin mappings from PDF schematics for FPGA development, reducing manual constraint writing effort.