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Reduced-Complexity Deep Neural Networks Design Using Multi-Level Compression

delete2019-04-01
delete6
PRE
AI
S
Siyu Liao
Y
Yi Xie
X
Xue Lin
Y
Yanzhi Wang
张
张民 (Min Zhang)
B
Bo Yuan *
DOI：10.1109/TSUSC.2017.2710178delete
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摘要

摘要

En 中文
深度神经网络已经在许多领域取得了巨大的成功。然而，许多DNN模型既深又大，从而导致在训练和推理阶段期间的高存储和能量消耗。本文提出了多级压缩框架。通过利用从结构压缩到权重压缩到表示压缩的跨层参数减少技术，所提出的压缩策略可以实现训练和推理的网络大小的数量级减少，而精度损失可以忽略不计，从而导致非常高效和高精度的DNN模型。实验表明，所提出的策略可以在密集矩阵方面实现约1.8K的压缩比，并且对于整个模型可以实现约30倍的压缩比。
Keyword:
Deep neural network
reduced complexity
compression

期刊

I
IEEE Transactions on Cloud Computing
IF:
5
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1.8K
被引数:
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city university of new york (cuny) system
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