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About

Yu-Hsin Chen is a prominent researcher at the forefront of energy-efficient hardware design for artificial intelligence and deep learning systems. Her work sits at the critical intersection of computer architecture, machine learning, and embedded systems, addressing one of the most pressing challenges in modern AI: the enormous computational cost of deploying deep neural networks in real-world applications. Chen is perhaps best known for her landmark survey, "Efficient Processing of Deep Neural Networks: A Tutorial and Survey" (2017), which has become an essential reference in the field with nearly 4,000 citations, cementing its status as a foundational resource for researchers and engineers alike. This work systematically examines techniques for optimizing DNN computation across hardware platforms, from data centers to edge devices. Her research also tackles the practical challenge of bringing computer vision capabilities to resource-constrained embedded systems, exploring how to close the energy efficiency gap between traditional and deep learning-based vision features for applications in robotics, autonomous vehicles, and wearable electronics. Through her contributions, Chen has meaningfully shaped how the research community approaches the design of intelligent, power-efficient hardware, making advanced AI more accessible and deployable across a wide spectrum of real-world platforms.