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Dual Precision Deep Neural Network

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 نشر من قبل Jae Hyun Park
 تاريخ النشر 2020
  مجال البحث الهندسة المعلوماتية
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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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