“Designing Deep Neural Network Algorithms for Embedded Devices,” a Presentation from Intel

Minje Park, Software Engineering Manager at Intel, presents the "Designing Deep Neural Network Algorithms for Embedded Devices" tutorial at the May 2017 Embedded Vision Summit.

Deep neural networks have shown state-of-the-art results in a variety of vision tasks. Although accurate, most of these deep neural networks are computationally intensive, creating challenges for embedded devices. In this talk, Park provides several ideas and insights on how to design deep neural network architectures small enough for embedded deployment. He also explores how to further reduce the processing load by adopting simple but effective compression and quantization techniques. He shows a set of practical applications, such as face recognition, facial attribute classification, and person detection, which can be run in near real-time without any heavy GPU or dedicated DSP and without losing accuracy.

Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.



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