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Fixed Point Quantization of Deep Convolutional Networks
[article]
2016
arXiv
pre-print
In recent years increasingly complex architectures for deep convolution networks (DCNs) have been proposed to boost the performance on image recognition tasks. However, the gains in performance have come at a cost of substantial increase in computation and model storage resources. Fixed point implementation of DCNs has the potential to alleviate some of these complexities and facilitate potential deployment on embedded hardware. In this paper, we propose a quantizer design for fixed point
arXiv:1511.06393v3
fatcat:v6amoxpaojhm3iparrldbsbjly