high performance computing on graphics processing units: hgpu.org

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BENCHIP: Benchmarking Intelligence Processors

Jin-Hua Tao, Zi-Dong Du, Qi Guo, Hui-Ying Lan, Lei Zhang, Sheng-Yuan Zhou, Cong Liu, Hai-Feng Liu, Shan Tang, Allen Rush, Willian Chen, Shao-Li Liu, Yun-Ji Chen, Tian-Shi Chen

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Tags: ASIC, Benchmarking, Computer science, Deep learning, nVidia, nVidia GeForce GTX 950, nVidia Tegra TX1, Tesla K40

October 24, 2017 by hgpu

Evaluation of GPU-based track-triggering for the CMS detector at CERN’s HL-LHC

Hannes Mohr

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Tags: Algorithms, ASIC, CUDA, FPGA, HEP, nVidia, Physics, Tesla K40, Tesla K80, Thesis

April 20, 2017 by hgpu

Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC

Eriko Nurvitadhi, David Sheffield, Jaewoong Sim, Asit Mishra, Ganesh Venkatesh, Debbie Marr

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Tags: Algorithms, ASIC, Computer science, CUDA, Deep learning, FPGA, Neural networks, nVidia, nVidia GeForce GTX Titan X, nVidia Tegra TX1, Performance

February 14, 2017 by hgpu

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

Ao Ren, Ji Li, Zhe Li, Caiwen Ding, Xuehai Qian, Qinru Qiu, Bo Yuan, Yanzhi Wang

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Tags: ASIC, CNN, Computer science, Deep learning, FPGA, Neural networks, nVidia, Tesla C2075

November 22, 2016 by hgpu

Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off

Hokchhay Tann, Soheil Hashemi, R. Iris Bahar, Sherief Reda

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Tags: Algorithms, ARM, ASIC, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia Tegra TX1

July 20, 2016 by hgpu

Hardware Acceleration for Unstructured Big Data and Natural Language Processing

Prateek Tandon

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Tags: ASIC, big data, Computer science, FPGA, Hadoop, hardware acceleration, Machine learning, MapReduce, NLP, Thesis

February 2, 2016 by hgpu

Origami: A Convolutional Network Accelerator

Lukas Cavigelli, David Gschwend, Christoph Mayer, Samuel Willi, Beat Muheim, Luca Benini

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Tags: Algorithms, Artificial intelligence, ASIC, Computer science, Computer vision, Machine learning, Neural networks

December 15, 2015 by hgpu

TABLA: A Unified Template-based Framework for Accelerating Statistical Machine Learning

Divya Mahajan, Jongse Park, Emmanuel Amaro, Hardik Sharma, Amir Yazdanbakhsh, Joon Kim, Hadi Esmaeilzadeh

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Tags: ARM, ASIC, Computer science, FPGA, Machine learning, nVidia, nVidia GeForce GTX 650 Ti, nVidia Jetson TK1, Performance, Tesla K40

September 29, 2015 by hgpu

RadixBoost: A Hardware Acceleration Structure for Scalable Radix Sort on Graphic Processors

Xingyu Liu, Shikai Li, Kuan Fang, Yufei Ni, Zonghui Li, Yangdong Deng

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Tags: Algorithms, ASIC, Computer science, FPGA, nVidia, nVidia GeForce GTX 580, Sorting

March 12, 2015 by hgpu

FastTree: A Hardware KD-Tree Construction Acceleration Engine for Real-Time Ray Tracing

Xingyu Liu, Yangdong Deng, Yufei Ni, Zonghui Li

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Tags: Algorithms, ASIC, Computer science, FPGA, KD-tree, Raytracing, Rendering

March 12, 2015 by hgpu

FPGA Acceleration of Multifunction Printer Image Processing using OpenCL

Timothy M. Hunter, Dmitry Denisenko, Sarnath Kannan, Jared M. Bold, Prasanthi Thippabathini, Peter Dusel P.E.

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Tags: ASIC, FPGA, Image processing, OpenCL

August 5, 2014 by hgpu

Bitcoin and The Age of Bespoke Silicon

Michael Bedford Taylor

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Tags: ASIC, ATI, Finance, FPGA, nVidia, OpenCL, Performance, Review

August 31, 2013 by hgpu

Page 1 of 212 »

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

CL-kmeans: Efficient k-Means in OpenCL

Efficient and Scalable k-Means on GPUs

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

pyJac: a Python package that generates source code used to analytically calculate chemical kinetics Jacobian matrices

Using SIMD and SIMT vectorization to evaluate sparse chemical kinetic Jacobian matrices and thermochemical source terms

CUTLASS: CUDA Templates for Linear Algebra Subroutines

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs

Integer sorting on multicore

A study of integer sorting on multicores

Kernel Tuner: A search-optimizing GPU code auto-tuner

libxsmm: Library targeting Intel Architecture for specialized dense and sparse matrix operations, and deep learning primitives

Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures

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10-12

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International Joint Conference on Robotics and Artificial Intelligence (JCRAI), 2018

2018

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13-15

Wellington, New Zealand

International Joint Conference on Computer Vision and Pattern Recognition (CCVPR), 2018

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high performance computing on graphics processing units: hgpu.org

BENCHIP: Benchmarking Intelligence Processors

Evaluation of GPU-based track-triggering for the CMS detector at CERN’s HL-LHC

Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off

Hardware Acceleration for Unstructured Big Data and Natural Language Processing

Origami: A Convolutional Network Accelerator

TABLA: A Unified Template-based Framework for Accelerating Statistical Machine Learning

RadixBoost: A Hardware Acceleration Structure for Scalable Radix Sort on Graphic Processors

FastTree: A Hardware KD-Tree Construction Acceleration Engine for Real-Time Ray Tracing

FPGA Acceleration of Multifunction Printer Image Processing using OpenCL

Bitcoin and The Age of Bespoke Silicon

Recent source codes

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

CL-kmeans: Efficient k-Means in OpenCL

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

pyJac: a Python package that generates source code used to analytically calculate chemical kinetics Jacobian matrices

CUTLASS: CUDA Templates for Linear Algebra Subroutines

Integer sorting on multicore

Kernel Tuner: A search-optimizing GPU code auto-tuner

libxsmm: Library targeting Intel Architecture for specialized dense and sparse matrix operations, and deep learning primitives

Most viewed papers (last 30 days)

Featured events

International Conference on Image, Video and Signal Processing (IVSP), 2019

International Joint Conference on Signals, Systems and Computers (CSSC), 2018

International Joint Conference on Robotics, Automation and Mechatronics (JCRAM), 2018

International Joint Conference on Robotics and Artificial Intelligence (JCRAI), 2018

International Joint Conference on Computer Vision and Pattern Recognition (CCVPR), 2018