Many computer vision applications rely on accurate and efficient neural networks. For years, we’ve been manually designing and optimizing neural networks to meet the accuracy and efficiency requirements. However, the productivity of such manual design flow is extremely low — It can take months to develop one model and optimize it for one target computing platform. Limited by productivity, we can only afford to design one neural network but deploy it on many computing platforms, even though this leads to sub-optimal performance. To solve this problem, we propose a scalable, easy-to-use, and automated framework for designing & optimizing neural networks. With small computing costs, this framework automatically designs highly accurate models customized for each computing platform with superior efficiency.
Bichen Wu is a PhD student at UC Berkeley, where he focuses on deep learning, computer vision, and autonomous driving.
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