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FastDeepIoT: Towards Understanding and Optimizing Neural Network
  Execution Time on Mobile and Embedded Devices

FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices

19 September 2018
Shuochao Yao
Yiran Zhao
Huajie Shao
Shengzhong Liu
Dongxin Liu
Lu Su
Tarek Abdelzaher
    HAI
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Papers citing "FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices"

2 / 2 papers shown
Title
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB
  model size
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
F. Iandola
Song Han
Matthew W. Moskewicz
Khalid Ashraf
W. Dally
Kurt Keutzer
96
7,448
0
24 Feb 2016
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained
  Quantization and Huffman Coding
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
Song Han
Huizi Mao
W. Dally
3DGS
121
8,793
0
01 Oct 2015
1