Low-power AI chip cuts drone identification energy use by 88.7%

Doyeon Kim, an undergraduate researcher in the Department of Electronic and Electrical Engineering, has published a paper in an academic journal. The research focuses on implementing drone artificial intelligence (AI) identification technology through low-power semiconductors. This study could dramatically improve the battery efficiency of systems that monitor unauthorized drones in real time in military and industrial environments. The research is published in the journal IEEE Transactions on Industrial Informatics.

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