SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks

CVPR 2018 Julian FaraoneNicholas FraserMichaela BlottPhilip H. W. Leong

Inference for state-of-the-art deep neural networks is computationally expensive, making them difficult to deploy on constrained hardware environments. An efficient way to reduce this complexity is to quantize the weight parameters and/or activations during training by approximating their distributions with a limited entry codebook... (read more)

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