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.
@article{arxiv.2009.02191,
title = {Dual Precision Deep Neural Network},
author = {Jae Hyun Park and Ji Sub Choi and Jong Hwan Ko},
journal= {arXiv preprint arXiv:2009.02191},
year = {2024}
}