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ChainerMN: Scalable Distributed Deep Learning Framework

Takuya Akiba, Keisuke Fukuda, Shuji Suzuki

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X, Package, Python

November 5, 2017 by hgpu

On Pre-Trained Image Features and Synthetic Images for Deep Learning

Stefan Hinterstoisser, Vincent Lepetit, Paul Wohlhart, Kurt Konolige

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Tags: Computer science, Computer vision, Deep learning, OpenGL, Rendering

October 31, 2017 by hgpu

Early Results of Deep Learning on the Stampede2 Supercomputer

Zhao Zhang, Weijia Xu, Niall Gaffney, Daniel Stanzione

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Tags: Caffe, Computer science, Deep learning, Intel Xeon Phi, TensorFlow

October 29, 2017 by hgpu

Deep Voice 3: 2000-Speaker Neural Text-to-Speech

Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan O. Arik, Ajay Kannan, Sharan Narang, Jonathan Raiman, John Miller

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Tags: Computer science, CUDA, Deep learning, nVidia, Speech recognition, Tesla P100

October 24, 2017 by hgpu

BENCHIP: Benchmarking Intelligence Processors

Jin-Hua Tao, Zi-Dong Du, Qi Guo, Hui-Ying Lan, Lei Zhang, Sheng-Yuan Zhou, Cong Liu, Hai-Feng Liu, Shan Tang, Allen Rush, Willian Chen, Shao-Li Liu, Yun-Ji Chen, Tian-Shi Chen

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Tags: ASIC, Benchmarking, Computer science, Deep learning, nVidia, nVidia GeForce GTX 950, nVidia Tegra TX1, Tesla K40

October 24, 2017 by hgpu

Revisiting the Case of ARM SoCs in High-Performance Computing Clusters

Tyler Fox

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX 960, nVidia Tegra TX1, SoC, Thesis

October 21, 2017 by hgpu

Synkhronos: a Multi-GPU Theano Extension for Data Parallelism

Adam Stooke, Pieter Abbeel

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Tags: Computer science, CUDA, Data parallelism, Deep learning, nVidia, nVidia DGX-1, Package, Tesla P100, Theano

October 15, 2017 by hgpu

FPGA implementation of a Convolutional Neural Network for "Wake up word" detection

Ole Martin Skafsa

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Tags: CNN, Computer science, Deep learning, FPGA, Machine learning, Neural networks, NLP, nVidia, nVidia Tegra TX1, OpenCL, Thesis

October 3, 2017 by hgpu

Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices

Zongqing Lu, Swati Rallapalli, Kevin Chan, Thomas La Porta

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia Jetson TK1, nVidia Tegra TX1

September 28, 2017 by hgpu

IBM Deep Learning Service

Bishwaranjan Bhattacharjee, Scott Boag, Chandani Doshi, Parijat Dube, Ben Herta, Vatche Ishakian, K. R. Jayaram, Rania Khalaf, Avesh Krishna, Yu Bo Li, Vinod Muthusamy, Ruchir Puri, Yufei Ren, Florian Rosenberg, Seetharami R. Seelam, Yandong Wang, Jian Ming Zhang, Li Zhang

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Tags: Cloud, Computer science, CUDA, Deep learning, Heterogeneous systems, Machine learning, Neural networks, nVidia

September 21, 2017 by hgpu

Distributed Training Large-Scale Deep Architectures

Shang-Xuan Zou, Chun-Yen Chen, Jui-Lin Wu, Chun-Nan Chou, Chia-Chin Tsao, Kuan-Chieh Tung, Ting-Wei Lin, Cheng-Lung Sung, Edward Y. Chang

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Data parallelism, Deep learning, nVidia, Tesla K80

September 21, 2017 by hgpu

Meta Networks for Neural Style Transfer

Falong Shen, Shuicheng Yan, Gang Zeng

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Tags: Caffe, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X, Package

September 16, 2017 by hgpu

high performance computing on graphics processing units: hgpu.org

ChainerMN: Scalable Distributed Deep Learning Framework

On Pre-Trained Image Features and Synthetic Images for Deep Learning

Early Results of Deep Learning on the Stampede2 Supercomputer

Deep Voice 3: 2000-Speaker Neural Text-to-Speech

BENCHIP: Benchmarking Intelligence Processors

Revisiting the Case of ARM SoCs in High-Performance Computing Clusters

Synkhronos: a Multi-GPU Theano Extension for Data Parallelism

FPGA implementation of a Convolutional Neural Network for "Wake up word" detection

Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices

Distributed Training Large-Scale Deep Architectures

Meta Networks for Neural Style Transfer

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