Deploying Deep Learning Models on Mobile NPUs:  What's New in 2026?

09:00 Denver Time   ┈   Andrey Ignatov   ┈   AI Benchmark Project Lead, ETH Zurich

Biography: Andrey Ignatov is the project lead of the AI Benchmark initiative targeted at performance evaluation of mobile, IoT and desktop hardware at ETH Zurich, Switzerland. His PhD research was aimed at designing efficient image processing models for smartphone NPUs / DSPs and developing the next-generation deep learning based mobile camera ISP solution. He is the lecturer on Deep Learning for Smartphones course at ETH Zurich and the main author of the AI Benchmark papers describing the current state of deep learning and AI hardware acceleration on mobile devices. He is a co-founder of the AI Witchlabs and co-organizer of the NTIRE and AIM events. His main line of research is focused on image restoration and automatic image quality enhancement, adaptation of AI applications for mobile devices and benchmarking machine learning hardware.

Abstract: In this tutorial, we will review the recent Android AI software stack updates, and will talk about the performance of the latest mobile chipsets from Qualcomm, MediaTek, Google, Samsung and Unisoc released during the past year. We will also discuss the power efficiency of mobile chipsets and their NPUs, and will analyze their energy consumption for a number of typical AI workloads.

Turning Computer Vision into Premium Experiences at the Edge

09:30 Denver Time   ┈   Felix Baum   ┈   Director Product Management, Qualcomm Technologies

Biography: Felix Baum is responsible for AI Software Products at Qualcomm Technologies Inc. (QTI). Felix has spent 20+ years in the embedded industry, both as an embedded developer and as a product manager. He previously led QTI product management for the Hexagon Software supporting DSPs with scalar, vector and tensor accelerators for camera, video, machine learning and audio verticals. Prior to that, he led marketing and product management efforts for various real-time operating system technologies. His career began at NASA's Jet Propulsion Laboratory at the California Institute of Technology, designing flight software for various spacecraft and managing integration for a launch campaign of the GRACE mission. Felix holds a master's degree in computer science from the California State University at Northridge and a Master of Business Administration from the University of California at Los Angeles.

Abstract: Most of the traditional and agentic AI-based mobile use cases revolve around camera and computer vision technologies. In this session, I'll kick things off by highlighting some of these applications, then expand the discussion to include ambient-based technologies and their use cases. We'll take a brief look at trends in adjacent markets, such as XR glasses and explore how computer vision and machine learning are elevating user experiences. Finally, I'll wrap up by describing the options developers have for designing, debugging and deploying workloads on hardware—ranging from community-driven frameworks to custom offerings.