high performance computing on graphics processing units: hgpu.org

Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean

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Tags: Computer science, CUDA, Deep learning, LSTM, Machine learning, NLP, nVidia, Tesla K80

September 27, 2016 by hgpu

Bridging the Semantic Gaps of GPU Acceleration for Scaleout CNN-based Big Data Processing: Think Big, See Small

Mingcong Song, Yang Hu, Yunlong Xu, Chao Li, Huixiang Chen, Jingling Yuan, Tao Li

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Tags: Analytical model, big data, Computer science, CUDA, Deep learning, Distributed computing, Neural networks, nVidia, Tesla K20

September 22, 2016 by hgpu

Characterization of Speech Recognition Systems on GPU Architectures

Albert Segura Salvador

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Tags: Algorithms, Computer science, CUDA, Deep learning, GPGPU-sim, nVidia, nVidia GeForce GTX 980, Speech recognition, Thesis

September 22, 2016 by hgpu

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high performance computing on graphics processing units: hgpu.org

LightRNN: Memory and Computation-Efficient Recurrent Neural Networks

GPflow: A Gaussian process library using TensorFlow

Machine Learning Based Intrusion Detection in Controller Area Networks

Neural Network Computing Using On-Chip Accelerators

Implementation of Frequency Domain Convolution for the Caffe-Framework

Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks

Distributed Training of Deep Neuronal Networks: Theoretical and Practical Limits of Parallel Scalability

Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Bridging the Semantic Gaps of GPU Acceleration for Scaleout CNN-based Big Data Processing: Think Big, See Small

Characterization of Speech Recognition Systems on GPU Architectures

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