Deep learning on mobile
Who is this presentation for?
- Software developers
Over the last few years, CNNs have risen in popularity, especially in the area of computer vision. Many mobile applications running on smartphones and wearable devices could 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.
Siddha Ganju and Meher Kasam explain practically how to bring the power of convolutional neural networks and deep learning to memory- and power-constrained devices like smartphones. You’ll learn various strategies to circumvent obstacles and build mobile-friendly, shallow CNN architectures that significantly reduce the memory footprint and therefore make them easier to store on a smartphone. Siddha and Meher also dive into how to use a family of model compression techniques to prune the network size for live-image processing, enabling you to build a CNN version optimized for inference on mobile devices. Along the way, you’ll learn practical strategies to preprocess your data in a manner that makes the models more efficient in the real world.
- A basic understanding of deep learning
What you'll learn
- Learn how to optimize a model for minimum latency and maximum speed
Siddha Ganju is a self-driving solutions architect at NVIDIA. Previously, she developed deep learning models for resource-constrained edge devices at Deep Vision. Her prior work ranges from visual question answering to generative adversarial networks to gathering insights from CERN’s petabyte-scale data. She was recently featured on Forbes‘s 30 under 30 list, and she’s been published at top-tier conferences including CVPR and NeurIPS. Serving as an AI domain expert, she’s also been guiding teams at NASA as well as featured as a jury member in several international tech competitions. She’s a graduate of Carnegie Mellon University.
Meher Kasam is an iOS software engineer at Square and is a seasoned software developer with apps used by tens of millions of users every day. He’s shipped features for a range of apps from Square’s point of sale to the Bing app. Previously, he worked at Microsoft, where he was the mobile development lead for the Seeing AI app, which has received widespread recognition and awards from Mobile World Congress, CES, FCC, and the American Council of the Blind, to name a few. A hacker at heart with a flair for fast prototyping, he’s won close to two dozen hackathons and converted them to features shipped in widely used products. He also serves as a judge of international competitions including the Global Mobile Awards and the Edison Awards.
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