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Scalable and Sustainable Deep Learning via Randomized Hashing
[article]
2016
arXiv
pre-print
Current deep learning architectures are growing larger in order to learn from complex datasets. These architectures require giant matrix multiplication operations to train millions of parameters. Conversely, there is another growing trend to bring deep learning to low-power, embedded devices. The matrix operations, associated with both training and testing of deep networks, are very expensive from a computational and energy standpoint. We present a novel hashing based technique to drastically
arXiv:1602.08194v2
fatcat:fo2pjpzsivgzxnmxurmlalfvda