Design of power-efficient approximate multipliers for approximate artificial neural networks

Article Details
Title:	Design of power-efficient approximate multipliers for approximate artificial neural networks
Article URLs:	https://doi.org/10.1145/2966986.2967021
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Authors:	Vojtech Mrazek	Brno University of Technology, Faculty of Information Technology Brno University of Technology, Centre of Excellence IT4Innovations
	Syed Shakib Sarwar	Purdue University, School of Electrical and Computer Engineering
	Lukás Sekanina	Brno University of Technology, Faculty of Information Technology Brno University of Technology, Centre of Excellence IT4Innovations
	Zdenek Vasícek	Brno University of Technology, Faculty of Information Technology Brno University of Technology, Centre of Excellence IT4Innovations
	Kaushik Roy	Purdue University, School of Electrical and Computer Engineering
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Verification:	Authors have not verified information
Artifact Evaluation Badge:	none
Artifact URLs:	https://github.com/rasmusbergpalm/DeepLearnToolbox https://github.com/nyanp/tiny-cnn
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NSF Award Numbers:	unknown
DBLP Key:	conf/iccad/MrazekSSV016
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ACM International Conference on Computer-Aided Design, ICCAD 2016