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Learning both Weights and Connections for Efficient Neural Networks

neural information processing systems, (2015): 1135-1143

Cited by: 4090|Views399
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Abstract

Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks fix the architecture before training starts; as a result, training cannot improve the architecture. To address these limitations, we describe a method to reduce the storage and computati...More

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