13403

maxDNN: An Efficient Convolution Kernel for Deep Learning with Maxwell GPUs

Andrew Lavin
eBay Research Labs Machine Learning
arXiv:1501.06633 [cs.NE], (27 Jan 2015)

@article{lavin2015maxdnn,

   title={maxDNN: An Efficient Convolution Kernel for Deep Learning with Maxwell GPUs},

   author={Lavin, Andrew},

   year={2015},

   month={jan},

   archivePrefix={"arXiv"},

   primaryClass={cs.NE}

}

Download Download (PDF)   View View   Source Source   Source codes Source codes

Package:

3965

views

This paper describes maxDNN, a computationally efficient convolution kernel for deep learning with the NVIDIA Maxwell GPU. maxDNN reaches 96.3% computational efficiency on typical deep learning network architectures using a single kernel. The design combines ideas from cuda-convnet2 with the Maxas SGEMM assembly code. We only address forward propagation (FPROP) operation of the network, but we believe that the same techniques used here will be effective for backward propagation (BPROP) as well.
Rating: 2.4/5. From 5 votes.
Please wait...

* * *

* * *

HGPU group © 2010-2024 hgpu.org

All rights belong to the respective authors

Contact us: