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. Accordingly, KEYWORDS | ASIC; computer architecture; convolutional techniques that enable Efficient Processing of DNNs to improve Neural networks; dataflow Processing ; deep learning; deep energy efficiency and throughput without sacrificing application Neural networks; energy- Efficient accelerators; low power.

ral networks, which is the focus of this article.1 B. Neural Networks and DNNs Neural networks take their inspiration from the notion that a neuron’s computation involves a weighted sum of the input values. These weighted sums correspond to the value scaling performed by the synapses and the combining of those values in the neuron.

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