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Dual Precision Deep Neural Network

2020-09-02Code Available0· sign in to hype

Jae Hyun Park, Ji Sub Choi, Jong Hwan Ko

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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.

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