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Explore low-power computer vision technologies in this 31-minute tinyML Talks webcast featuring Yung-Hsiang Lu from Purdue University. Discover energy-efficient solutions for battery-powered systems, including parameter quantization, compressed convolutional filters, network architecture search, and knowledge distillation. Learn about hierarchical neural networks for reducing energy consumption on embedded systems and get introduced to the IEEE International Low-Power Computer Vision Challenge. Gain insights into recent research, challenges, and open questions in the field of low-power computer vision.
