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

IEEE Journal of Solid-State Circuits

Volumn 52, Issue 1, 2017, Pages 127-138

  Chen, Yu Hsin ^a   Krishna, Tushar ^a,b   Emer, Joel S ^a,c   Sze, Vivienne ^a  

^a USA   (United States)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^c NVIDIA   (United States)

Author keywords

Convolutional neural networks (CNNs); dataflow processing; deep learning; energy efficient accelerators; spatial architecture

Indexed keywords

COMPUTER ARCHITECTURE; CONVOLUTION; DATA COMPRESSION; NETWORK ARCHITECTURE; NEURAL NETWORKS; RECONFIGURABLE HARDWARE;

CONVOLUTIONAL NEURAL NETWORK; DATA MOVEMENTS; ENERGY EFFICIENT; ENTIRE SYSTEM; HIGH THROUGHPUT; PROCESSING ELEMENTS; RECONFIGURABLE; STATE OF THE ART;

ENERGY EFFICIENCY;

EID: 84995478886     PISSN: 00189200     EISSN: None     Source Type: Journal    
DOI: 10.1109/JSSC.2016.2616357     Document Type: Article

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