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A Practical Guide to Getting the DNN Accuracy You Need and the Performance You Deserve


Date:  Wednesday, May 18 (Main Conference Day 2)

Start Time: 2:40 pm

End Time: 3:10 pm

Every day, developers struggle to take DNN workloads that were originally developed on workstations and migrate them to run on edge devices. Whether the application is in mobile, compute, IoT, XR or automotive, most AI developers start their algorithm development in the cloud or on a workstation and later migrate to on-device as an afterthought. Qualcomm is helping these developers on multiple fronts—democratizing AI at the edge by supporting frameworks and data types that developers are most familiar with, and at the same time building a set of tools to assist sophisticated developers who are taking extra steps to extract the best performance and power efficiency. In this session, we present the workflow and steps for effectively migrating DNN workloads to the edge. We discuss quantization issues, explore how the accuracy of models affects performance and power and outline the Qualcomm tools that help developers successfully launch new use cases on mobile and other edge devices.

Track

Session Speakers

  • 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 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.


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A Practical Guide to Getting the DNN Accuracy You Need and the Performance You Deserve