Learning with few bits on small-scale devices: From regularization to energy efficiency

22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings

Volumn , Issue , 2014, Pages 81-86

  Anguita, Davide ^a   Ghio, Alessandro ^a   Oneto, Luca ^a   Ridella, Sandro ^a  

^a UNIVERSITY OF GENOA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; ELECTRIC BATTERIES; ENERGY EFFICIENCY; NEURAL NETWORKS;

BATTERY CAPACITY; HUMAN ACTIVITY RECOGNITION; PROCESSING POWER; RADEMACHER COMPLEXITY; REMOTE COMPUTING; RESOURCE-LIMITED DEVICES; SMALL SCALE DEVICES; SMALL-SCALE SYSTEMS;

LEARNING SYSTEMS;

EID: 84962019150     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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