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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

Deep, Dense, and Low-Rank Gaussian Conditional Random Fields

Siddhartha Chandra, Iasonas Kokkinos

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Tags: Caffe, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 1080

November 30, 2016 by hgpu

A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications

Matthew W. Moskewicz, Ali Jannesari, Kurt Keutzer

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Tags: AMD R9 Nano, ARM, ATI, Code generation, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package

November 23, 2016 by hgpu

Diplomat: Mapping of multi-kernel applications using a static dataflow abstraction

Bruno Bodin, Luigi Nardi, Paul H. J. Kelly, Michael F. P. O'Boyle

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Tags: ARM, Computer science, Computer vision, Heterogeneous systems, OpenCL

November 3, 2016 by hgpu

Programming Heterogeneous Systems from an Image Processing DSL

Jing Pu, Steven Bell, Xuan Yang, Jeff Setter, Stephen Richardson, Jonathan Ragan-Kelley, Mark Horowitz

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Tags: ARM, Computer science, Computer vision, CUDA, DSL, FPGA, Image processing, nVidia, nVidia Jetson TK1, OpenGL, Package

November 1, 2016 by hgpu

Embedded real-time stereo estimation via Semi-Global Matching on the GPU

Daniel Hernandez-Juarez, Alejandro Chacon, Antonio Espinosa, David Vazquez, Juan Carlos Moure, Antonio Manuel Lopez

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

October 15, 2016 by hgpu

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

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

GPU-based Parallel Computation Support for Stan

GPU-based Parallel Computation Support for Stan

PANNA: Properties from Artificial Neural Network Architectures

PANNA: Properties from Artificial Neural Network Architectures

Artifact Evaluation for Novel methodologies for predictable CPU-to-GPU command offloading

Novel Methodologies for Predictable CPU-To-GPU Command Offloading

tvm: Open deep learning compiler stack for cpu, gpu and specialized accelerators

A Unified Optimization Approach for CNN Model Inference on Integrated GPUs

picongpu: Particle-in-Cell Simulations for the Exascale Era

PIConGPU: Predictive Simulations of Laser-Particle Accelerators with Manycore Hardware

WCCV: Warp-coherent Condition Vectorization(on top of RV)

WCCV: Improving the Vectorization of IF-statements with Warp-Coherent Conditions

Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations

Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations

HEATS: Heterogeneity- and Energy-Aware Task-based Scheduling

HEATS: Heterogeneity- and Energy-Aware Task-based Scheduling

HAM: Heterogeneous Active Messages C++ library

Heterogeneous Active Messages (HAM) – Implementing Lightweight Remote Procedure Calls in C++

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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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