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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

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

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice

HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

BAT: A standardized benchmark suite for auto-tuners

BAT: A Benchmark suite for AutoTuners

The Darmstadt Automotive Parallel HeterogeNEous (DAPHNE) Benchmark-Suite

Programming Heterogeneous Systems with General and Domain-Specific Frameworks

General purpose lattice QCD code set Bridge++ 2.0 for high performance computing

MQBench: Towards Reproducible and Deployable Model Quantization Benchmark

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

iGUARD: In-GPU Advanced Race Detection

TaPaSCo: The Task Parallel System Composer

Optimizing a Hardware Network Stack to Realize an In-Network ML Inference Application

Tapenade Automatic Differentiation Engine

Source-to-Source Automatic Differentiation of OpenMP Parallel Loops

Colossal-AI: an integrated large-scale model training system with efficient parallelization techniques

Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training

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high performance computing on graphics processing units: hgpu.org

Applications

Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs

Accelerating CNN inference on FPGAs: A Survey

A Survey of FPGA Based Neural Network Accelerator

On Pre-Trained Image Features and Synthetic Images for Deep Learning

Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices

Memory-Efficient Implementation of DenseNets

Scalability Study of Deep Learning Algorithms in High Performance Computer Infrastructures

A Fast Method For Computing Principal Curvatures From Range Images

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

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs

Implementing Efficient, Portable Computations for Machine Learning

Recent source codes

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice

BAT: A standardized benchmark suite for auto-tuners

The Darmstadt Automotive Parallel HeterogeNEous (DAPHNE) Benchmark-Suite

General purpose lattice QCD code set Bridge++ 2.0 for high performance computing

MQBench: Towards Reproducible and Deployable Model Quantization Benchmark

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

iGUARD: In-GPU Advanced Race Detection

TaPaSCo: The Task Parallel System Composer

Tapenade Automatic Differentiation Engine

Colossal-AI: an integrated large-scale model training system with efficient parallelization techniques

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