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Computation on Sparse Neural Networks: an Inspiration for Future Hardware
[article]
2020
arXiv
pre-print
Neural network models are widely used in solving many challenging problems, such as computer vision, personalized recommendation, and natural language processing. Those models are very computationally intensive and reach the hardware limit of the existing server and IoT devices. Thus, finding better model architectures with much less amount of computation while maximally preserving the accuracy is a popular research topic. Among various mechanisms that aim to reduce the computation complexity,
arXiv:2004.11946v1
fatcat:2lnbtmi4grb65nxcxab4kz6pvy