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Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

Murad Qasaimeh, Kristof Denolf, Jack Lo, Kees Vissers, Joseph Zambreno, Phillip H. Jones

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Tags: ARM, Benchmarking, Computer science, Computer vision, CUDA, FPGA, nVidia, nVidia Jetson TX2, OpenCV, Package

May 26, 2019 by hgpu

Performance Analysis of Deep Learning Workloads on Leading-edge Systems

Yihui Ren, Shinjae Yoo, Adolfy Hoisie

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Tags: Cloud, Computer science, Computer vision, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia DGX-2, nVidia GeForce RTX 2080 Ti, Performance, Tesla V100

May 23, 2019 by hgpu

Real world applications of Artificial Intelligence on constrained hardware

Wiebe Van Ranst

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Tags: Artificial intelligence, Computer science, Computer vision, Image processing, nVidia, nVidia Jetson TX2, OpenCL, Thesis

April 20, 2019 by hgpu

Accelerated Neural Networks on OpenCL Devices Using SYCL-DNN

Rod Burns, John Lawson, Duncan McBain, Daniel Soutar

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Tags: Computer science, Computer vision, Deep learning, Machine learning, Neural networks, nVidia, OpenCL, Package, SYCL

April 14, 2019 by hgpu

Full-System Simulation of Mobile CPU/GPU Platforms

Kuba Kaszyk, Harry Wagstaff, Tom Spink, Bjorn Franke, Mike O'Boyle, Bruno Bodin

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Tags: Computer science, Computer vision, OpenCL

April 7, 2019 by hgpu

DeeperLab: Single-Shot Image Parser

Tien-Ju Yang, Maxwell D. Collins, Yukun Zhu, Jyh-Jing Hwang, Ting Liu, Xiao Zhang, Vivienne Sze, George Papandreou, Liang-Chieh Chen

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Tags: Computer science, Computer vision, Deep learning, nVidia, Tesla V100

February 17, 2019 by hgpu

Performance Analysis of a Stereo Matching Implementation in OpenCL

Stephan Rotheneder

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX 750 Ti, nVidia Quadro 1000 M, OpenCL, Stereo vision

December 16, 2018 by hgpu

A Framework for Fast and Efficient Neural Network Compression

Hyeji Kim, Muhammad Umar Karim, Chong-Min Kyung

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Tags: ARM, Caffe, Compression, Computational Complexity, Computer science, Computer vision, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti

December 9, 2018 by hgpu

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

Francisco M. Castro, Nicolas Guil, Manuel J. Marin-Jimenez, Jesus Perez-Serrano, Manuel Ujaldon

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Tags: Artificial intelligence, CNN, Computer science, Computer vision, CUDA, Deep learning, Energy-efficient computing, Neural networks, nVidia, nVidia GeForce GTX Titan X

August 5, 2018 by hgpu

Comparing Two Generations of Embedded GPUs Running a Feature Detection Algorithm

Max Danielsson, Hakan Grahn, Thomas Sievert, Jim Rasmusson

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Tags: Computer science, Computer vision, OpenCL

June 20, 2018 by hgpu

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

Kernel Tuner

Bayesian Optimization for auto-tuning GPU kernels

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

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high performance computing on graphics processing units: hgpu.org

Applications

Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

Real world applications of Artificial Intelligence on constrained hardware

Accelerated Neural Networks on OpenCL Devices Using SYCL-DNN

Full-System Simulation of Mobile CPU/GPU Platforms

DeeperLab: Single-Shot Image Parser

Performance Analysis of a Stereo Matching Implementation in OpenCL

A Framework for Fast and Efficient Neural Network Compression

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

Comparing Two Generations of Embedded GPUs Running a Feature Detection Algorithm

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

Neural Multi-scale Image Compression

Recent source codes

Kernel Tuner

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

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