Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks

Proceedings - 2016 43rd International Symposium on Computer Architecture, ISCA 2016

Volumn , Issue , 2016, Pages 367-379

  Chen, Yu Hsin ^a   Emer, Joel ^a,b   Sze, Vivienne ^a  

^a Cambridge ^*  (United States)

^b NVIDIA   (United States)

Author keywords

Convolutional Neural Networks; Dataflow; Energy Efficiency; Spatial Architecture

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPLEX NETWORKS; COMPUTER ARCHITECTURE; CONVOLUTION; COST BENEFIT ANALYSIS; COSTS; DATA FLOW ANALYSIS; DATA HANDLING; DIGITAL STORAGE; ENERGY UTILIZATION; NETWORK ARCHITECTURE; NEURAL NETWORKS;

ANALYSIS FRAMEWORKS; CONVOLUTIONAL NEURAL NETWORK; DATAFLOW; FABRICATED CHIPS; HIGH-DIMENSIONAL; PARALLEL PROCESSING; PROCESSING ENGINE; SPATIAL PARALLELISM;

ENERGY EFFICIENCY;

EID: 84988317007     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISCA.2016.40     Document Type: Conference Paper

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