Eager pruning: algorithm and architecture support for fast training of deep neural networks

Today's big and fast data and the changing circumstance require fast training of Deep Neural Networks (DNN) in various applications. However, training a DNN with tons of parameters involves intensive computation. Enlightened by the fact that redundancy exists in DNNs and the observation that th...

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Bibliographic Details
Main Authors:	Zhang, Jiaqi, Chen, Xiangru, Song, Mingcong, Li, Tao
Format:	Conference Proceeding
Language:	English
Subjects:	Computer systems organization Computer systems organization > Architectures Computer systems organization > Architectures > Other architectures Computer systems organization > Architectures > Other architectures > Data flow architectures Computer systems organization > Architectures > Other architectures > Neural networks Computing methodologies Computing methodologies > Machine learning Computing methodologies > Machine learning > Machine learning approaches Computing methodologies > Machine learning > Machine learning approaches > Neural networks Hardware Hardware > Emerging technologies Hardware > Emerging technologies > Analysis and design of emerging devices and systems Hardware > Emerging technologies > Analysis and design of emerging devices and systems > Emerging architectures Neural Network Pruning Neural Network Training Software-Hardware Co-Design
Online Access:	Request full text
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