Minimizing Computation in Convolutional Neural Networks
Lecture notes in computer sciencePublished 1 January 2014
Jason Cong, Bingjun Xiao
Citations369
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
This work analyzes the linear algebraic properties of CNNs and proposes an algorithmic modification to reduce their computational workload, achieving up to a 47% reduction.
Abstract
Convolutional Neural Networks (CNNs) have been successfully used for many computer vision applications. It would be beneficial to these applications if the computational workload of CNNs could be reduced. In this work we analyze the linear algebraic properties of CNNs and propose an algorithmic modification to reduce their computational workload. An up to a 47% reduction can be achieved without any change in the image recognition results or the addition of any hardware accelerators.
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Computer ScienceEngineering
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