论文标题
双精度深神经网络
Dual Precision Deep Neural Network
论文作者
论文摘要
深神经网络（DNN）的在线精确可伸缩性是支持DNN推断期间准确性和复杂性权衡的关键特征。在本文中，我们提出了双精度DNN，其中包含单个模型中的两种不同的精度模式，从而支持在线精确开关而无需重新训练。提出的两阶段训练过程优化了低精度和高精度模式。
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.
