Sep 23–26, 2019


Siddha Ganju (Nvidia), Meher Kasam (Square)
4:35pm5:15pm Wednesday, September 25, 2019
Location: 1A 06/07
Secondary topics:  Data Integration and Data Processing, Deep Learning, Financial Services

Who is this presentation for?

Software dev




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.

This workshop explains how to practically bring the power of convolutional neural networks and deep learning to memory and power-constrained devices like smartphones. You will 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; The workshop also dives 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 will learn practical strategies to preprocess your data in a manner that makes the models more efficient in the real world.

Prerequisite knowledge

Basics of Deep Learning.

What you'll learn

How to optimize a model for minimum latency and maximum speed.
Photo of Siddha Ganju

Siddha Ganju


Siddha Ganju, who Forbes featured in their 30 under 30 list, is a Self-Driving Architect at Nvidia. Previously at Deep Vision, she developed deep learning models for resource constraint edge devices. A graduate from Carnegie Mellon University, her prior work ranges from Visual Question Answering to Generative Adversarial Networks to gathering insights from CERN’s petabyte-scale data and has been published at top-tier conferences including CVPR and NeurIPS. Serving as an AI domain expert, she has also been guiding teams at NASA as well as featured as a jury member in several international tech competitions.

Photo of Meher Kasam

Meher Kasam


Meher is a seasoned software developer with apps used by tens of millions of users every day. Currently at Square, and previously at Microsoft, he shipped features for a range of apps, from Square’s Point of Sale to the Bing app. He was the mobile development lead for Microsoft’s Seeing AI app, which has received widespread recognition and awards from Mobile World Congress, CES, FCC, American Council of the Blind to name a few. A hacker at heart with a flair for fast prototyping, he has 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 Global Mobile Awards, Edison Awards.

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