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Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

Francisco M. Castro, Nicolas Guil, Manuel J. Marin-Jimenez, Jesus Perez-Serrano, Manuel Ujaldon

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Tags: Artificial intelligence, CNN, Computer science, Computer vision, CUDA, Deep learning, Energy-efficient computing, Neural networks, nVidia, nVidia GeForce GTX Titan X

August 5, 2018 by hgpu

Comparing Two Generations of Embedded GPUs Running a Feature Detection Algorithm

Max Danielsson, Hakan Grahn, Thomas Sievert, Jim Rasmusson

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Tags: Computer science, Computer vision, OpenCL

June 20, 2018 by hgpu

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

Neural Multi-scale Image Compression

Ken Nakanishi, Shin-ichi Maeda, Takeru Miyato, Daisuke Okanohara

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Tags: Compression, Computer vision, CUDA, Deep learning, Image processing, Machine learning, nVidia

May 20, 2018 by hgpu

Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs

Chuanhao Zhuge, Xinheng Liu, Xiaofan Zhang, Sudeep Gummadi, Jinjun Xiong, Deming Chen

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Tags: CNN, Computer science, Computer vision, FFT, FPGA, HLS

March 31, 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

A Survey of FPGA Based Neural Network Accelerator

Kaiyuan Guo, Shulin Zeng, Jincheng Yu, Yu Wang, Huazhong Yang

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Tags: Computer science, Computer vision, FPGA, Machine learning, Neural networks

December 28, 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

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

Memory-Efficient Implementation of DenseNets

Geoff Pleiss, Danlu Chen, Gao Huang, Tongcheng Li, Laurens van der Maaten, Kilian Q. Weinberger

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Tags: Computer science, Computer vision, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X, Package, Tesla M40, Torch

July 25, 2017 by hgpu

Scalability Study of Deep Learning Algorithms in High Performance Computer Infrastructures

Francesc Sastre Cabot

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Tags: Algorithms, Computer science, Computer vision, CUDA, Deep learning, GPU cluster, Machine learning, Neural networks, nVidia, Tesla K80, Tesla M2090, Thesis

July 22, 2017 by hgpu

A Fast Method For Computing Principal Curvatures From Range Images

Andrew Spek, Wai Ho Li, Tom Drummond

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Tags: Computer science, Computer vision, CUDA, nVidia, Package, Tesla K40

July 5, 2017 by hgpu

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Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

CL-kmeans: Efficient k-Means in OpenCL

Efficient and Scalable k-Means on GPUs

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

pyJac: a Python package that generates source code used to analytically calculate chemical kinetics Jacobian matrices

Using SIMD and SIMT vectorization to evaluate sparse chemical kinetic Jacobian matrices and thermochemical source terms

CUTLASS: CUDA Templates for Linear Algebra Subroutines

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs

Integer sorting on multicore

A study of integer sorting on multicores

Kernel Tuner: A search-optimizing GPU code auto-tuner

libxsmm: Library targeting Intel Architecture for specialized dense and sparse matrix operations, and deep learning primitives

Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures

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high performance computing on graphics processing units: hgpu.org

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

Comparing Two Generations of Embedded GPUs Running a Feature Detection Algorithm

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

Neural Multi-scale Image Compression

Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs

Accelerating CNN inference on FPGAs: A Survey

A Survey of FPGA Based Neural Network Accelerator

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

Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices

Memory-Efficient Implementation of DenseNets

Scalability Study of Deep Learning Algorithms in High Performance Computer Infrastructures

A Fast Method For Computing Principal Curvatures From Range Images

Recent source codes

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

CL-kmeans: Efficient k-Means in OpenCL

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

pyJac: a Python package that generates source code used to analytically calculate chemical kinetics Jacobian matrices

CUTLASS: CUDA Templates for Linear Algebra Subroutines

Integer sorting on multicore

Kernel Tuner: A search-optimizing GPU code auto-tuner

libxsmm: Library targeting Intel Architecture for specialized dense and sparse matrix operations, and deep learning primitives

Most viewed papers (last 30 days)

Featured events

International Conference on Image, Video and Signal Processing (IVSP), 2019

International Joint Conference on Signals, Systems and Computers (CSSC), 2018

International Joint Conference on Robotics, Automation and Mechatronics (JCRAM), 2018

International Joint Conference on Robotics and Artificial Intelligence (JCRAI), 2018

International Joint Conference on Computer Vision and Pattern Recognition (CCVPR), 2018