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Asynchronous Methods for Deep Reinforcement Learning

Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

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Tags: Algorithms, Computer science, Deep learning, Machine learning, Neural networks, nVidia, Performance, Tesla K40

February 6, 2016 by hgpu

EIE: Efficient Inference Engine on Compressed Deep Neural Network

Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, William J. Dally

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia Jetson TK1, Sparse matrix

February 6, 2016 by hgpu

Deep Learning For Smile Recognition

Patrick O. Glauner

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Tesla K40

February 3, 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

Extracting Flow Features Using Bag-of-Features and Supervised Learning Techniques

Yifei Li

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Source codes

Tags: Algorithms, Clustering, Computer science, Computer vision, CUDA, Machine learning, nVidia, nVidia GeForce GTX 580, OpenMP, Package, Thesis, Visualization

February 2, 2016 by hgpu

Deep learning review and its applications

Andoni Azkarate Saiz

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 660, Review, Thesis

February 1, 2016 by hgpu

GPU-Accelerated Recurrent Neural Networks: OpenCLLink and SymbolicC

Zdenek Buk

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

January 29, 2016 by hgpu

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

Francisco Javier Ordonez, Daniel Roggen

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Source codes

Tags: Computer science, Deep learning, LSTM, Machine learning, Neural networks, Package, Python, RNN, Theano

January 26, 2016 by hgpu

Adaptive and Hybrid Machine Learning Approaches Utilizing General Purpose Computing on Graphical Processing Units

Mohammed Abdelrahim

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Tags: Computer science, CUDA, Heterogeneous systems, LSTM, Machine learning, nVidia, nVidia GeForce GTX 770, OpenMP, Thesis

January 19, 2016 by hgpu

Classification of Higgs Boson Tau-Tau decays using GPU accelerated Neural Networks

Mohit Shridhar

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Tags: CUDA, Deep learning, High Energy Physics - Phenomenology, Machine learning, Neural networks, nVidia, Physics

January 14, 2016 by hgpu

DeepLearningKit – an Open Source Deep Learning Framework for Apple’s iOS, OS X and tvOS developed in Metal and Swift

Amund Tvei, Torbjorn Morland, Thomas Brox Rost

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Source codes

Tags: Computer science, Deep learning, Machine learning, Neural networks, OpenCL, Package, Vulkan

January 7, 2016 by hgpu

Automatic Performance Tuning of Stencil Computations on Graphics Processing Units

Joseph D. Garvey

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Tags: Computer science, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Performance, Stencil computation, Thesis

January 4, 2016 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

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high performance computing on graphics processing units: hgpu.org

Asynchronous Methods for Deep Reinforcement Learning

EIE: Efficient Inference Engine on Compressed Deep Neural Network

Deep Learning For Smile Recognition

Hardware Acceleration for Unstructured Big Data and Natural Language Processing

Extracting Flow Features Using Bag-of-Features and Supervised Learning Techniques

Deep learning review and its applications

GPU-Accelerated Recurrent Neural Networks: OpenCLLink and SymbolicC

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

Adaptive and Hybrid Machine Learning Approaches Utilizing General Purpose Computing on Graphical Processing Units

Classification of Higgs Boson Tau-Tau decays using GPU accelerated Neural Networks

DeepLearningKit – an Open Source Deep Learning Framework for Apple’s iOS, OS X and tvOS developed in Metal and Swift

Automatic Performance Tuning of Stencil Computations on Graphics Processing Units

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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