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CBinfer: Change-Based Inference for Convolutional Neural Networks on Video Data

Lukas Cavigelli, Philippe Degen, Luca Benini

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Tags: Artificial intelligence, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX Titan X, nVidia Tegra TX1

April 17, 2017 by hgpu

Cascaded Segmentation-Detection Networks for Word-Level Text Spotting

Siyang Qin, Roberto Manduchi

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Tags: Algorithms, Caffe, Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX Titan X

April 7, 2017 by hgpu

Large-scale image analysis using docker sandboxing

B. Sengupta, E. Vazquez, M. Sasdelli, Y. Qian, M. Peniak, L. Netherton, G. Delfino

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Tags: Computer science, Computer vision, CUDA, Image processing, nVidia, nVidia Quadro M 6000

March 14, 2017 by hgpu

Optimizing Deep CNN-Based Queries over Video Streams at Scale

Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia

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Tags: Computer science, Computer vision, Databases, Deep learning, Machine learning, Neural networks, nVidia, nVidia DGX-1, Package, TensorFlow, Tesla P100

March 9, 2017 by hgpu

Speckle Reduction with Trained Nonlinear Diffusion Filtering

Wensen Feng, Yunjin Chen

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Tags: CNN, Computer vision, Filtering, Image processing, Matlab, nVidia, nVidia GeForce GXT 780 Ti, Package, SAR

February 28, 2017 by hgpu

Improving the Performance of OpenCL-based FPGA Accelerator for Convolutional Neural Network

Jialiang Zhang, Jing Li

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

February 26, 2017 by hgpu

Efficient Large-scale Approximate Nearest Neighbor Search on the GPU

Patrick Wieschollek, Oliver Wang, Alexander Sorkine-Hornung, Hendrik P.A. Lensch

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Tags: Computer science, Computer vision, CUDA, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, Vector quantization

February 22, 2017 by hgpu

Backpropagation Training for Fisher Vectors within Neural Networks

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

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Tags: Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX Titan X

February 10, 2017 by hgpu

Efficient Neural Network Acceleration on GPGPU using Content Addressable Memory

Mohsen Imani, Daniel Peroni, Yeseong Kim, Abbas Rahimi, Tajana Rosing

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Computer vision, Image processing, Neural networks, OpenCL

February 2, 2017 by hgpu

Deep Convolutional Network evaluation on the Intel Xeon Phi: Where Subword Parallelism meets Many-Core

Gaurav Raina

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Tags: Computer science, Computer vision, Deep learning, Intel Xeon Phi, Neural networks, OpenMP, Thesis

January 26, 2017 by hgpu

An OpenCL(TM) Deep Learning Accelerator on Arria 10

Utku Aydonat, Shane O'Connell, Davor Capalija, Andrew C. Ling, Gordon R. Chiu

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Tags: CNN, Computer science, Computer vision, Deep learning, FPGA, OpenCL, Performance

January 16, 2017 by hgpu

Streaming GPU Singular Value and Dynamic Mode Decompositions

Seth D. Pendergrass, J. Nathan Kutz, Steven L. Brunton

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Tags: Algorithms, Computer vision, CUDA, Mathematics, Matrix multiplication, Numerical Analysis, nVidia, Sparse matrix, Tesla K40

December 26, 2016 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

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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

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