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Quantifying the Energy Efficiency of Object Recognition and Optical Flow

Michael Anderson, Forrest Iandola, Kurt Keutzer

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Tags: Algorithms, ATI, ATI Radeon HD 7990, Computer science, Neural networks, OpenCL, Optical flow

April 7, 2014 by hgpu

RASR/NN: The RWTH Neural Network Toolkit for Speech Recognition

Simon Wiesler, Alexander Richard, Pavel Golik, Ralf Schluter, Hermann Ney

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Tags: Algorithms, Computer science, CUDA, Neural networks, nVidia, Speech recognition, Tesla K20

March 9, 2014 by hgpu

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Ying Zhang, Saizheng Zhang

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Tags: Computer science, CUBLAS, CUDA, Machine learning, Neural networks, nVidia

February 2, 2014 by hgpu

Implementing Neural Networks Efficiently

Ronan Collobert, Koray Kavukcuoglu, Clement Farabet

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Tags: Algorithms, Computer science, CUDA, Machine learning, Neural networks, nVidia, Tesla M2090

December 29, 2013 by hgpu

GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

Thomas Paine, Hailin Jin, Jianchao Yang, Zhe Lin, Thomas Huang

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Tags: Computer science, Computer vision, CUDA, Data parallelism, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla K20

December 24, 2013 by hgpu

Single Server Multi-GPU Training of ConvNets

Omry Yadan, Keith Adams, Yaniv Taigman, Marc'Aurelio Ranzato

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Tags: Computer science, Computer vision, Machine learning, Neural networks

December 23, 2013 by hgpu

GPU and CPU Cooperative Accelerated Road Detection

Peng Xiong, Cheng Xu, Zheng Tian, Tao Li

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Tags: Computer science, Computer vision, Neural networks, OpenCV

December 3, 2013 by hgpu

A Novel CPU/GPU Simulation Environment for Large-Scale Biologically-Realistic Neural Modeling

Roger V. Hoang, Devyani Tanna, Laurence C. Jayet Bray, Sergiu M. Dascalu, Frederick C. Harris Jr.

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Tags: Biology, CUDA, Neural networks, Neuroscience, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Package, Tesla C2050

September 14, 2013 by hgpu

GPU-based simulation of brain neuron models

H. A. Du Nguyen

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Tags: CUDA, Neural networks, Neuroscience, nVidia, nVidia GeForce GT 640, Review, Tesla C2075, Thesis

September 7, 2013 by hgpu

Multiple Time Scales Recurrent Neural Network for Complex Action Acquisition

Martin Peniak, Davide Marocco, Jun Taniy, Yuichi Yamashitay, Kerstin Fischerz, Angelo Cangelosi

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Tags: Computer science, CUDA, Neural networks, nVidia, Package

August 27, 2013 by hgpu

Characterization of Lossy SIW Resonators Based on Multilayer Perceptron Neural Networks on Graphics Processing Unit

Giandomenico Amendola, Giovanni Angiulli, Emilio Arnieri, Luigi Boccia, Domenico De Carlo

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Tags: CUDA, Electrodynamics, Neural networks, nVidia, Tesla C2070

July 25, 2013 by hgpu

GPU Implementation of Real-Time Biologically Inspired Face Detection using CUDA

Zeinab Farhoudi, Ali Broumandnia, Elham Askary, Sara Motamed

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Tags: Computer science, Computer vision, CUDA, Image processing, Neural networks, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce 9800 GTX

July 8, 2013 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

Quantifying the Energy Efficiency of Object Recognition and Optical Flow

RASR/NN: The RWTH Neural Network Toolkit for Speech Recognition

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Implementing Neural Networks Efficiently

GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

Single Server Multi-GPU Training of ConvNets

GPU and CPU Cooperative Accelerated Road Detection

A Novel CPU/GPU Simulation Environment for Large-Scale Biologically-Realistic Neural Modeling

GPU-based simulation of brain neuron models

Multiple Time Scales Recurrent Neural Network for Complex Action Acquisition

Characterization of Lossy SIW Resonators Based on Multilayer Perceptron Neural Networks on Graphics Processing Unit

GPU Implementation of Real-Time Biologically Inspired Face Detection using CUDA

Recent source codes

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

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