ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks

@article{Gudovskiy2017ShiftCNNGL,
  title={ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks},
  author={Denis A. Gudovskiy and Luca Rigazio},
  journal={CoRR},
  year={2017},
  volume={abs/1706.02393}
}
In this paper we introduce ShiftCNN, a generalized low-precision architecture for inference of multiplierless convolutional neural networks (CNNs). ShiftCNN is based on a power-of-two weight representation and, as a result, performs only shift and addition operations. Furthermore, ShiftCNN substantially reduces computational cost of convolutional layers by precomputing convolution terms. Such an optimization can be applied to any CNN architecture with a relatively small codebook of weights and… CONTINUE READING
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