Deep Learning on Mobile

Duration: 50 mins
Siddha Ganju
Self-driving Architect, NVIDIA

Over the last few years, convolutional neural networks (CNN) have risen in popularity, especially in the area of computer vision. Many mobile applications running on smartphones and wearable devices would potentially benefit from the new opportunities enabled by deep learning techniques. However, CNNs are by nature computationally and memory intensive, making them challenging to deploy on a mobile device. We explain how to practically bring the power of convolutional neural networks and deep learning to memory and power-constrained devices like smartphones. We’ll illustrate the value of these concepts with real-time demos as well as case studies from Google, Microsoft, Facebook and more. You will walk away with various strategies to circumvent obstacles and build mobile-friendly shallow CNN architectures that significantly reduce memory footprint.

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