high performance computing on graphics processing units: hgpu.org

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A High-efficiency FPGA-based Accelerator for Convolutional Neural Networks using Winograd Algorithm

Y. Huang, J. Shen, Z. Wang, M. Wen, C. Zhang

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Tags: Computational Complexity, Computer science, Computer vision, Deep learning, FPGA, HLS, Neural networks

June 9, 2018 by hgpu

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

Byungsoo Kim, Vinicius C. Azevedo, Nils Thuerey, Theodore Kim, Markus Gross, Barbara Solenthaler

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Tags: cfd, Deep learning, Fluid dynamics, Fluid simulation, Neural networks, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X, TensorFlow

June 9, 2018 by hgpu

Design of FPGA-Based Accelerator for Convolutional Neural Network under Heterogeneous Computing Framework with OpenCL

Li Luo, Yakun Wu, Fei Qiao, Yi Yang, Qi Wei, Xiaobo Zhou, Yongkai Fan, Shuzheng Xu, Xinjun Liu, Huazhong Yang

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Tags: Algorithms, CNN, Computer science, Deep learning, FPGA, Heterogeneous systems, Neural networks, OpenCL

June 2, 2018 by hgpu

Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe

Jiong Gong, Haihao Shen, Guoming Zhang, Xiaoli Liu, Shane Li, Ge Jin, Niharika Maheshwari, Evarist Fomenko, Eden Segal

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Tags: Caffe, Cloud, Computer science, Deep learning, Intel Xeon Phi, Neural networks, Package

May 26, 2018 by hgpu

Glow: Graph Lowering Compiler Techniques for Neural Networks

Nadav Rotem, Jordan Fix, Saleem Abdulrasool, Summer Deng, Roman Dzhabarov, James Hegeman, Roman Levenstein, Bert Maher, Satish Nadathur, Jakob Olesen, Jongsoo Park, Artem Rakhov, Misha Smelyanskiy

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Tags: Code generation, Compilers, Computer science, CUDA, Deep learning, Heterogeneous systems, Linear Algebra, Machine learning, Neural networks, nVidia, Package

May 20, 2018 by hgpu

CANNA: Neural Network Acceleration using Configurable Approximation on GPGPU

Mohsen Imani, Max Masich, Daniel Peroni, Pushen Wang, Tajana Rosing

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Tags: AMD Radeon HD 7970, ATI, Computer science, Deep learning, Neural networks, OpenCL

April 22, 2018 by hgpu

CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++

Xiaolin Wang

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, OpenCV, Package

April 22, 2018 by hgpu

DLL: A Blazing Fast Deep Neural Network Library

Baptiste Wicht, Jean Hennebert, Andreas Fischer

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 960, Package, Software Engineering

April 15, 2018 by hgpu

Accelerating CNN inference on FPGAs: A Survey

Kamel Abdelouahab, Maxime Pelcat, Jocelyn Serot, Francois Berry

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Tags: Computer science, Computer vision, Deep learning, FPGA, HLS, Neural networks, OpenCL

March 25, 2018 by hgpu

TBD: Benchmarking and Analyzing Deep Neural Network Training

Hongyu Zhu, Mohamed Akrout, Bojian Zheng, Andrew Pelegris, Amar Phanishayee, Bianca Schroeder, Gennady Pekhimenko

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan XP, nVidia Quadro P 4000

March 22, 2018 by hgpu

Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures

Manolis Loukadakis, Jose Cano, Michael O'Boyle

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Tags: ARM, Computer science, Deep learning, Heterogeneous systems, Neural networks, OpenCL

February 10, 2018 by hgpu

Accelerating recurrent neural network language model based online speech recognition system

Kyungmin Lee, Chiyoun Park, Namhoon Kim, Jaewon Lee

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, RNN, Speech recognition, Tesla K80

February 3, 2018 by hgpu

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

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high performance computing on graphics processing units: hgpu.org

A High-efficiency FPGA-based Accelerator for Convolutional Neural Networks using Winograd Algorithm

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

Design of FPGA-Based Accelerator for Convolutional Neural Network under Heterogeneous Computing Framework with OpenCL

Glow: Graph Lowering Compiler Techniques for Neural Networks

CANNA: Neural Network Acceleration using Configurable Approximation on GPGPU

CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++

DLL: A Blazing Fast Deep Neural Network Library

Accelerating CNN inference on FPGAs: A Survey

TBD: Benchmarking and Analyzing Deep Neural Network Training

Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures

Accelerating recurrent neural network language model based online speech recognition system

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