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A High-efficiency FPGA-based Accelerator for Convolutional Neural Networks using Winograd Algorithm

Y. Huang, J. Shen, Z. Wang, M. Wen, C. Zhang

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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

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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

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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

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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

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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

Hierarchical Product Quantization for Approximate Nearest Neighbors Search

Accelerated Approximate Nearest Neighbors Search Through Hierarchical Product Quantization

hlslib: A collection of extensions for Vivado HLS and Intel FPGA OpenCL to improve developer quality of life

hlslib: Software Engineering for Hardware Design

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

MCL kernel verification library

Verification of GPU Program Optimizations in Lean

waLBerla: A block-structured high-performance framework for multiphysics simulations

waLBerla: A block-structured high-performance framework for multiphysics simulations

MIOpen: AMD's Machine Intelligence Library

MIOpen: An Open Source Library For Deep Learning Primitives

horovod: Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet

Exascale Deep Learning for Scientific Inverse Problems

Futhark Vulkan Backend

Futhark Vulkan Backend

Megatron-LM: Ongoing research training transformer language models at scale, including: BERT & GPT-2

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Espresso: A Fast End-to-End Neural Speech Recognition Toolkit

Espresso: A Fast End-to-end Neural Speech Recognition Toolkit

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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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