DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework

@article{Yao2017DeepIoTCD,
  title={DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework},
  author={Shuochao Yao and Yiran Zhao and Aston Zhang and Lu Su and Tarek F. Abdelzaher},
  journal={ArXiv},
  year={2017},
  volume={abs/1706.01215}
}
  • Shuochao Yao, Yiran Zhao, +2 authors Tarek F. Abdelzaher
  • Published in SenSys '17 2017
  • Computer Science, Mathematics
  • ArXiv
  • Recent advances in deep learning motivate the use of deep neutral networks in sensing applications, but their excessive resource needs on constrained embedded devices remain an important impediment. [...] Key Method First, unlike current solutions geared for compressing specific types of neural networks, DeepIoT presents a unified approach that compresses all commonly used deep learning structures for sensing applications, including fully-connected, convolutional, and recurrent neural networks, as well as their…Expand Abstract

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