Efficient Processing of Deep Neural Networks: A Tutorial ...

Efficient Processing of Deep Neural Networks: A Tutorial and Survey This article provides a comprehensive Tutorial and survey coverage of the recent advances toward enabling Efficient Processing of deep Neural networks. B y V i v i e n n e S z e , S e n i o r M e m b e r I E E E , Y u -H s i n C h e n , S t u d e n t M e m b e r I E E E , Ti e n -J u Ya ng , Student Member IEEE, a n d J oe l S. E m e r , Fellow IEEE. ABSTRACT | Deep Neural networks (DNNs) are currently widely between various hardware architectures and platforms;. used for many artificial intelligence (AI) applications including be able to evaluate the utility of various DNN design computer vision, speech recognition, and robotics. While DNNs techniques for Efficient Processing ; and understand recent deliver state-of-the-art accuracy on many AI tasks, it comes implementation trends and opportunities. at the cost of high computational complexity.

program) of the brain does not change. This characteristic makes the brain an excellent inspiration for a machine-learning-style algorithm. Within the brain-inspired computing paradigm there is a subarea called spiking computing. In this subarea, inspira-tion is taken from the fact that the communication on the

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