Researchers have developed AnalogRetriever, a novel framework designed to improve the searchability of analog circuit designs across different formats like netlists, schematics, and textual descriptions. This system utilizes a combination of a vision-language model for visual and textual data and a graph convolutional network for netlists, mapping them into a shared space for effective retrieval. AnalogRetriever demonstrated a 75.2% average Recall@1 across various cross-modal search tasks and has shown promise when integrated into agentic frameworks for circuit design. AI
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IMPACT Enhances analog circuit design workflows by enabling efficient cross-modal search and retrieval of existing IP.
RANK_REASON This is a research paper introducing a new retrieval framework for analog circuit designs.