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2018

A Quantization-Friendly Separable Convolution for MobileNets

Sheng, Tao, Feng, Chen, Zhuo, Shaojie et al.

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As deep learning (DL) is being rapidly pushed to edge computing, researchers invented various ways to make inference computation more efficient on mobile/IoT devices, such as network pruning, parameter compression, and etc.

Quantization, as one of the key approaches, can effectively offload GPU, and make it possible to deploy DL on fixed-point pipeline.
Unfortunately, not all existing networks design are friendly to quantization.
For example, the popular lightweight MobileNetV1, while it successfully reduces parameter size and computation latency with separable convolution, our experiment shows its quantized models have large accuracy gap against its float point models.

A Quantization-Friendly Separable Convolution for MobileNets · Around

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Udo Zölzer. Digital Audio Signal Processing , Chapter 2
1997
Earlier work this paper cites.
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition
Original
2014
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition
Original
2015
Earlier work this paper cites.

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