high performance computing on graphics processing units: hgpu.org

hgpu.org » Computer vision

DeeperLab: Single-Shot Image Parser

Tien-Ju Yang, Maxwell D. Collins, Yukun Zhu, Jyh-Jing Hwang, Ting Liu, Xiao Zhang, Vivienne Sze, George Papandreou, Liang-Chieh Chen

View

Tags: Computer science, Computer vision, Deep learning, nVidia, Tesla V100

February 17, 2019 by hgpu

Performance Analysis of a Stereo Matching Implementation in OpenCL

Stephan Rotheneder

View

Tags: Computer science, Computer vision, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX 750 Ti, nVidia Quadro 1000 M, OpenCL, Stereo vision

December 16, 2018 by hgpu

A Framework for Fast and Efficient Neural Network Compression

Hyeji Kim, Muhammad Umar Karim, Chong-Min Kyung

View

Tags: ARM, Caffe, Compression, Computational Complexity, Computer science, Computer vision, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti

December 9, 2018 by hgpu

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

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

View

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

View

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

View

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

View

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

View

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

View

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

View

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

View

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

View

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

September 28, 2017 by hgpu

libhclooc: Software Library Facilitating Out-of-core Implementations of Accelerator Kernels on Hybrid Computing Platforms

Novel Data-Partitioning Algorithms for Performance and Energy Optimization of Data-Parallel Applications on Modern Heterogeneous HPC Platforms

CLTestCheck: Measuring Test Effectiveness for GPU Kernels

CLTestCheck: Measuring Test Effectiveness for GPU Kernels

tensorflow-solvers

TensorFlow Doing HPC

CU2CL: A prototype CUDA-to-OpenCL source-to-source translator

On the Portability of GPU-Accelerated Applications via Automated Source-to-Source Translation

EngineCL: Usability and Performance in Heterogeneous Computing

Cooperative CPU, GPU, and FPGA heterogeneous execution with EngineCL

cuSten: CUDA Finite Difference Library

cuSten – CUDA Finite Difference and Stencil Library

ZMCintegral: An easy way to use multi-GPUs to calculate multi-dimensional integration

A Package for Multi-Dimensional Monte Carlo Integration on Multi-GPUs

TensorFlow.js: A WebGL accelerated JavaScript library for training and deploying ML models

TensorFlow.js: Machine Learning for the Web and Beyond

Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications

Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications

SHA3-ON-GPU: SHA3 implementation on CPU & GPU

GPU Accelerated Keccak (SHA3) Algorithm

See all packages

* * *

* * *

2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

+

HGPU group © 2010-2019 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: