In recent years, there has been lots of work done on low-precision inference that shows that by training for quantization, large gains in energy efficiency can be achieved. On the other hand, we have seen embedded runtime packages like TensorFlow Lite and Caffe2Go emerge that offer portability over a number of platforms. Cormac Brick look at the challenge presented by this choice and asks, “Why can’t we have both?” Cormac explains how big this gap truly is, using state-of-the-art methods for both and specifically trained networks to show performance over a range of popular vision applications. He then covers best-in-class design techniques for developing portable networks to maximize performance on different compute architectures variety of network architectures and shares industry challenges and progress needed to close the portability performance gap.
Cormac Brick is director of machine intelligence in the Movidius Group at Intel Corporation, where he builds new foundational algorithms for computer vision and machine intelligence to enhance the Myriad VPU product family. Cormac contributes to internal architecture and helps customers build products using the very latest techniques in deep learning and embedded vision through a set of advanced applications and libraries. He has worked with Movidius since its early days and has contributed heavily to the design of the ISA and the hardware systems as well as computer vision software development and tools. Cormac holds a BEng in electronic engineering from University College Cork.
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