1k citations · 1.2k across the 3 of their papers we have counts for
3 papers
DSD: Dense-Sparse-Dense Training for Deep Neural Networks
Song Han, Jeff Pool, Sharan Narang +9
Modern deep neural networks have a large number of parameters, making them very hard to train. We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural netw…
cuDNN: Efficient Primitives for Deep Learning
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch +4
We present a library of efficient implementations of deep learning primitives. Deep learning workloads are computationally intensive, and optimizing their kernels is difficult and…
Parallel Support Vector Machines in Practice
Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger +2
In this paper, we evaluate the performance of various parallel optimization methods for Kernel Support Vector Machines on multicore CPUs and GPUs. In particular, we provide the fir…