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hgpu.org » Applications » Computer science » Computer vision

PyMatting: A Python Library for Alpha Matting

Thomas Germer, Tobias Uelwer, Stefan Conrad, Stefan Harmeling

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Tags: Computer vision, CUDA, Image processing, nVidia, OpenCL, Package, PyOpenCL, Python

April 5, 2020 by hgpu

Parallelization of the Honeybee Search Algorithm for Object Tracking

Oscar E. Perez-Cham, Cesar Puente, Carlos Soubervielle-Montalvo, Gustavo Olague, Carlos A. Aguirre-Salado, Alberto S. Nuñez-Varela

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Tags: AMD Radeon R9 270, Artificial intelligence, ATI, Computer science, Computer vision, nVidia, nVidia GeForce GTX 1050, OpenCL, Particle swarm optimization

April 5, 2020 by hgpu

Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

Balazs Nagy, Philipp Foehn, Davide Scaramuzza

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Tags: Algorithms, Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GTX 960 M, nVidia Jetson TX2, OpenCV, Package

April 5, 2020 by hgpu

Accelerating CNN on FPGA: An Implementation of MobileNet on FPGA

Yulan Shen

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Tags: classification, Computer science, Computer vision, Deep learning, FPGA, Image processing, nVidia, nVidia GeForce GTX 940 M, Thesis

March 1, 2020 by hgpu

An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

Xiaolong Ma, Wei Niu, Tianyun Zhang, Sijia Liu, Fu-Ming Guo, Sheng Lin, Hongjia Li, Xiang Chen, Jian Tang, Kaisheng Ma, Bin Ren, Yanzhi Wang

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Tags: ARM, Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce RTX 2080 Ti, OpenCL, Package

January 26, 2020 by hgpu

Hardware Implementation and Quantization of Tiny-Yolo-v2 using OpenCL

Yap June Wai, Zulkalnain bin Mohd Yussof, Sani Irwan bin Md Salim

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

January 19, 2020 by hgpu

Towards High Performance Java-based Deep Learning Frameworks

Athanasios Stratikopoulos, Juan Fumero, Zoran Sevarac, Christos Kotselidis

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Tags: Computer science, Computer vision, CUDA, Data mining, Deep learning, Heterogeneous systems, Java, Machine learning, nVidia, OpenCL, Package

January 19, 2020 by hgpu

Atmospheric turbulence removal using convolutional neural network

Jing Gao, N. Anantrasirichai, David Bull

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Tags: Computer vision, Deep learning, Image processing, Neural networks, nVidia, Tesla P100

December 29, 2019 by hgpu

GGNN: Graph-based GPU Nearest Neighbor Search

Fabian Groh, Lukas Ruppert, Patrick Wieschollek, Hendrik P.A. Lensch

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Tags: Compter science, Computer vision, Deep learning, Nearest neighbour, nVidia, nVidia GeForce GTX 1080 Ti, nVidia Titan RTX

December 8, 2019 by hgpu

PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision

Tomasz Kornuta

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Tags: Computer science, Computer vision, Machine learning, Neural networks, Package, Python

October 27, 2019 by hgpu

Characterizing Deep Learning Training Workloads on Alibaba-PAI

Mengdi Wang, Chen Meng, Guoping Long, Chuan Wu, Jun Yang, Wei Lin, Yangqing Jia

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Tags: Artificial intelligence, Computer science, Computer vision, Deep learning, GPU cluster, nVidia, Tesla V100

October 20, 2019 by hgpu

Dynamic Distribution Pruning for Efficient Network Architecture Search

Xiawu Zheng, Rongrong Ji, Lang Tang, Yan Wan, Baochang Zhang, Yongjian Wu, Yunsheng Wu, Ling Shao

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, Package, Tesla V100

June 5, 2019 by hgpu

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Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

MapReduce for Counting Word Frequencies with MPI and GPUs

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

SYCLCOMP Utilities

On the Compilation Performance of Current SYCL Implementations

M1 GPUs for (scientific) computations in C++

Seamless GPU acceleration for C++ based physics with the Metal Shading Language on Apple’s M series unified chips

CUB: Cooperative primitives for CUDA C++

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

Simulator and workload generator for Edge-to-Cloud environments

FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

PyMatting: A Python Library for Alpha Matting

Parallelization of the Honeybee Search Algorithm for Object Tracking

Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

Accelerating CNN on FPGA: An Implementation of MobileNet on FPGA

An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

Hardware Implementation and Quantization of Tiny-Yolo-v2 using OpenCL

Towards High Performance Java-based Deep Learning Frameworks

Atmospheric turbulence removal using convolutional neural network

GGNN: Graph-based GPU Nearest Neighbor Search

PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision

Characterizing Deep Learning Training Workloads on Alibaba-PAI

Dynamic Distribution Pruning for Efficient Network Architecture Search

Recent source codes

Rodinia Benchmarks for DPC++

DCA++: a state of the art implementation of the dynamical cluster approximation

tntorch - Tensor Network Learning with PyTorch

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

MapReduce for Counting Word Frequencies with MPI and GPUs

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

SYCLCOMP Utilities

M1 GPUs for (scientific) computations in C++

CUB: Cooperative primitives for CUDA C++

Simulator and workload generator for Edge-to-Cloud environments

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