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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

High-Performance Out-of-core Block Randomized Singular Value Decomposition on GPU

Yuechao Lu, Fumihiko Ino, Yasuyuki Matsushita

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Tags: Algorithms, Computer science, Computer vision, CUDA, Linear Algebra, Machine learning, nVidia, Tesla P100

June 25, 2017 by hgpu

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Priya Goyal, Piotr Dollar, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla P100

June 10, 2017 by hgpu

Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs

Xiaoming Chen, Jianxu Chen, Danny Z. Chen, Xiaobo Sharon Hu

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Tags: CNN, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Tesla K40

June 1, 2017 by hgpu

Implementing Efficient, Portable Computations for Machine Learning

Matthew Walter Moskewicz

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Tags: Algorithms, AMD R9 Nano, ATI, Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Thesis

May 24, 2017 by hgpu

Real-Time Adaptive Image Compression

Oren Rippel, Lubomir Bourdev

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Tags: Algorithms, Compression, Computer science, Computer vision, Machine learning, nVidia, nVidia GeForce GTX 980 Ti

May 18, 2017 by hgpu

A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA

Xinyu Zhang, Srinjoy Das, Ojash Neopane, Ken Kreutz-Delgado

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

May 11, 2017 by hgpu

IPMACC: a framework for translating OpenACC for C API to CUDA, OpenCL, and Intel ISPC

Loop Perforation in OpenACC

multiQueryStream: Concurrent query processing in a GPU-based database system

Concurrent query processing in a GPU-based database system

SYCL-DNN: The SYCL-DNN neural network acceleration library

Accelerated Neural Networks on OpenCL Devices Using SYCL-DNN

OpenCL vs: Accelerated Finite-Difference Digital Synthesis

OpenCL vs: Accelerated Finite-Difference Digital Synthesis

SYCL-BLAS: An implementation of BLAS using the SYCL open standard for acceleration on OpenCL devices

Cross-Platform Performance Portability Using Highly Parametrized SYCL Kernels

High Performance Monte Carlo Simulation of Ising Model on TPU Clusters

High Performance Monte Carlo Simulation of Ising Model on TPU Clusters

The Study of the OpenCL Processing Models for the FPGA Device

The Study of the OpenCL Processing Models for the FPGA Devices

TonY: a framework to natively run deep learning frameworks on Apache Hadoop

TonY: An Orchestrator for Distributed Machine Learning Jobs

fairseq: Facebook AI Research Sequence-to-Sequence Toolkit written in Python

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

mARGOt: Dynamic Application Autotuning for Self-Aware Approximate Computing

Dynamic Application Autotuning for Self-Aware Approximate Computing

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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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