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Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks

Author(s)
Chen, Yu-Hsin; Krishna, Tushar; Emer, Joel S.; Sze, Vivienne
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Abstract
Deep learning using convolutional neural networks (CNN) gives state-of-the-art accuracy on many computer vision tasks (e.g. object detection, recognition, segmentation). Convolutions account for over 90% of the processing in CNNs for both inference/testing and training, and fully convolutional networks are increasingly being used. To achieve state-of-the-art accuracy requires CNNs with not only a larger number of layers, but also millions of filters weights, and varying shapes (i.e. filter sizes, number of filters, number of channels) as shown in Fig. 14.5.1. For instance, AlexNet [1] uses 2.3 million weights (4.6MB of storage) and requires 666 million MACs per 227×227 image (13kMACs/pixel). VGG16 [2] uses 14.7 million weights (29.4MB of storage) and requires 15.3 billion MACs per 224×224 image (306kMACs/pixel). The large number of filter weights and channels results in substantial data movement, which consumes significant energy.
Date issued
2016-02
URI
http://hdl.handle.net/1721.1/101151
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
IEEE International Conference on Solid-State Circuits (ISSCC 2016)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Chen, Yu-Hsin, Tushar Krishna, Joel Emer, and Vivienne Sze. "Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks." in ISSCC 2016, IEEE International Solid-State Circuits Conference, Jan. 31-Feb. 4, 2016. San Francisco, CA.
Version: Author's final manuscript
ISBN
978-1-4673-9467-3

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