Processor Options for Edge Inference: Options and Trade-offs

Wednesday, May 22, 4:20 PM - 4:50 PM
Summit Track: 
Business Insights
Location: 
Theater

Thanks to rapid advances in neural network algorithms, we’ve made tremendous progress in developing robust solutions for numerous computer vision tasks. Face detection, face recognition, object identification, object tracking, lane marking detection and pedestrian detection are just a few examples of challenging visual perception tasks where deep neural networks are providing superior solutions to traditional computer vision algorithms.

Compared with traditional algorithms, deep neural networks rely on a very different computational model. As a result, the types of processor architectures being used for deep neural networks are also quite different from those used in the past. In this talk, we will explore the diverse processor architecture approaches that are gaining popularity in machine learning-based embedded vision applications and discuss their strengths and weaknesses in general, and in the context of specific applications.

Speaker(s):

Raj Talluri

Senior Vice President and General Manager, Mobile Business Unit, Micron

Raj Talluri serves as senior vice president and general manager for the Mobile Business Unit of Micron. Previously, Raj was senior vice president of product management for Qualcomm Technologies, Inc. (QTI), where he was responsible for managing QTI’s Internet of Things (IoT) business. Prior to this role, he was responsible for product management of mobile computing, Sense ID 3D finger print technology and Qualcomm Snapdragon application processor technologies.

Talluri has more than 20 years of experience spanning across business management, strategic marketing and engineering management. Talluri holds a Ph.D. in electrical engineering from the University of Texas at Austin. He also holds a Master of Engineering degree from Anna University in Chennai, India and a Bachelor of Engineering from Andhra University in Waltair, India. He has published more than 35 journal articles, papers and book chapters in many leading electrical engineering publications. He has been granted 13 U.S. patents for image processing, video compression and media processor architectures.

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