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of 2
pro vyhledávání: '"Mellempudi, Naveen K."'
Sparse training is emerging as a promising avenue for reducing the computational cost of training neural networks. Several recent studies have proposed pruning methods using learnable thresholds to efficiently explore the non-uniform distribution of
Externí odkaz:
http://arxiv.org/abs/2304.06941
Autor:
Kundu, Abhisek, Heinecke, Alex, Kalamkar, Dhiraj, Srinivasan, Sudarshan, Qin, Eric C., Mellempudi, Naveen K., Das, Dipankar, Banerjee, Kunal, Kaul, Bharat, Dubey, Pradeep
We propose K-TanH, a novel, highly accurate, hardware efficient approximation of popular activation function TanH for Deep Learning. K-TanH consists of parameterized low-precision integer operations, such as, shift and add/subtract (no floating point
Externí odkaz:
http://arxiv.org/abs/1909.07729