@inproceedings{d9081a5acf494c20acfd98e5f402d9c4,
title = "Dual-Precision Deep Neural Network",
abstract = "On-line Precision scalability of the deep neural networks(DNNs) is a critical feature to support accuracy and complexity trade-off during the DNN inference. In this paper, we propose dual-precision DNN that includes two different precision modes in a single model, thereby supporting an on-line precision switch without re-training. The proposed two-phase training process optimizes both low- and high-precision modes.",
keywords = "Deep neural network, dual precision, precision scalable, weight quantization",
author = "Park, \{Jae Hyun\} and Choi, \{Ji Sub\} and Ko, \{Jong Hwan\}",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 3rd International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2020 ; Conference date: 26-06-2020 Through 28-06-2020",
year = "2020",
month = jun,
day = "26",
doi = "10.1145/3430199.3430228",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "30--34",
booktitle = "Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2020",
}