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.