A Deep Look into Logarithmic Quantization of Model Parameters in Neural Networks-Reference-Cited by-同舟云学术

A Deep Look into Logarithmic Quantization of Model Parameters in Neural Networks

Published:2018 Issue: Volume: Page:
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Container-title:Proceedings of the 10th International Conference on Advances in Information Technology - IAIT 2018
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Author:

Cai Jingyong¹,Takemoto Masashi²,Nakajo Hironori³

Affiliation:

1. Graduate School of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan

2. BeatCraft Inc, Tokyo, Japan

3. Institute of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan

Publisher

ACM Press

Reference29 articles.

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2. Jingyong Cai, Masashi Takemoto, and Hironori Nakajo. 2018. Implementation of DNN on a RISC-V Open Source Microprocessor for IoT Devices. In 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) (IEEE GCCE 2018). Nara, Japan.

3. Gal Chechik, Isaac Meilijson, and Eytan Ruppin. 1998. Synaptic Pruning in Development: A Computational Account. Neural Computation 10, 7 (1998), 1759--1777. https://doi.org/10.1162/089976698300017124 arXiv:https://doi.org/10.1162/089976698300017124

4. Jia Deng, Wei Dong, Richard Socher, Li jia Li, Kai Li, and Li Fei-fei. 2009. Imagenet: A large-scale hierarchical image database. In In CVPR.

5. Li Deng. 2012. The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]. 29 (11 2012), 141--142.

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