Hardware-oriented Approximation of Convolutional Neural Networks

@article{Gysel2016HardwareorientedAO,
  title={Hardware-oriented Approximation of Convolutional Neural Networks},
  author={Philipp Gysel and Mohammad Motamedi and Soheil Ghiasi},
  journal={CoRR},
  year={2016},
  volume={abs/1604.03168}
}
High computational complexity hinders the widespread usage of Convolutional Neural Networks (CNNs), especially in mobile devices. Hardware accelerators are arguably the most promising approach for reducing both execution time and power consumption. One of the most important steps in accelerator development is hardware-oriented model approximation. In this paper we present Ristretto, a model approximation framework that analyzes a given CNN with respect to numerical resolution used in… CONTINUE READING
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Dally , and Kurt Keutzer . Squeezenet : Alexnetlevel accuracy with 50 x fewer parameters and ¡ 1 mb model size

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