Deep Learning on Mobile Devices


Date:  Thursday, September 24, 2020

Start Time: 1:00 pm

End Time: 2:00 pm

Over the last few years, convolutional neural networks (CNNs) have grown enormously in popularity, especially for computer vision. Many applications running on smartphones and wearable devices could benefit from the capabilities of CNNs. However, CNN’s are by nature computation- and memory-intensive, making them challenging to deploy on a mobile device. This session presents easy-to-use, practical techniques that can improve CNN performance on mobile devices.

Track

Session Speakers

  • Siddha Ganju, an AI researcher who Forbes featured in their 30 under 30 list, is a Self-Driving Architect at Nvidia. As an AI Advisor to NASA FDL, she helped build an automated meteor detection pipeline for the CAMS project, which discovered a comet. Previously at Deep Vision, she developed deep learning models for resource-constrained edge devices. Her 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. She has served as a featured jury member in several international tech competitions including CES. As an advocate for diversity and inclusion in technology, she speaks at schools and colleges to motivate and grow a new generation of technologists from all backgrounds


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Deep Learning on Mobile Devices