A Runtime Switchable Multi-Phase Convolutional Neural Network for Resource-Constrained Systems
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, Ajou University, Suwon-si, South Korea
2. Department of Electrical and Computer Engineering, Santa Clara University, Santa Clara, CA, USA
Funder
Institute of Information Communications Technology Planning Evaluation
Korea government (MSIT) through the project “Neuromorphic Computing Software Platform for Artificial Intelligence Systems”
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10156849.pdf?arnumber=10156849
Reference42 articles.
1. MobileNets: Efficient convolutional neural networks for mobile vision applications;howard;arXiv 1704 04861,2017
2. Cnvlutin
3. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size;iandola;arXiv 1602 07360,2016
4. Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding;han;arXiv 1510 00149 [cs],2015
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