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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

IODA: an Input/Output Deep Architecture for image labeling

J. Lerouge, R. Herault, Clement Chatelain, F. Jardin, R. Modzelewski

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Python, Tesla C1060, Theano

July 18, 2016 by hgpu

OpenFace: A general-purpose face recognition library with mobile applications

Brandon Amos, Bartosz Ludwiczuk, Mahadev Satyanarayanan

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Tags: Computer science, Computer vision, CUDA, Deep learning, Lua, Neural networks, nVidia, Package, Python, Tesla K40

July 13, 2016 by hgpu

Using Deep Convolutional Neural Networks in Monte Carlo Tree Search

Tobias Graf, Marco Platzner

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Tags: Algorithms, CNN, Computer science, CUDA, Deep learning, Neural networks, nVidia, Search, Tesla K20

July 11, 2016 by hgpu

Deep Learning for Mortgage Risk

Justin Sirignano, Apaar Sadhwani, Kay Giesecke

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Tags: CUDA, Deep learning, Finance, Machine learning, Neural networks, nVidia, Statistical Finance

July 11, 2016 by hgpu

DeepBE: Learning Deep Binary Encoding for Multi-Label Classification

Chenghua Li, Qi Kang, Guojing Ge, Qiang Song, Hanqing Lu, Jian Cheng

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Tags: Computer science, Computer vision, CUDA, Deep learning, Image recognition, Neural networks, nVidia, nVidia GeForce GTX Titan Black, nVidia GeForce GTX Titan X

June 30, 2016 by hgpu

Persistent RNNs: Stashing Recurrent Weights On-Chip

Gregory Diamos, Shubho Sengupta, Bryan Catanzaro, Mike Chrzanowski, Adam Coates, Erich Elsen, Jesse Engel, Awni Hannun, Sanjeev Satheesh

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Tags: Computer science, CUDA, Deep learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX Titan X, Package, RNN, Speech recognition

June 30, 2016 by hgpu

CNNLab: a Novel Parallel Framework for Neural Networks using GPU and FPGA-a Practical Study with Trade-off Analysis

Maohua Zhu, Liu Liu, Chao Wang, Yuan Xie

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Tags: CNN, Computer science, CUDA, Deep learning, FPGA, MapReduce, Neural networks, nVidia, OpenCL, Tesla K40

June 22, 2016 by hgpu

YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights

Renzo Andri, Lukas Cavigelli, Davide Rossi, Luca Benini

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Tags: Computer science, Computer vision, Deep learning, Hardware Architecture, Neural networks

June 21, 2016 by hgpu

cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL

Hugh Perkins

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Tags: Algorithms, CNN, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GRID K520, OpenCL, Package

June 21, 2016 by hgpu

Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs

Stefan Hadjis, Ce Zhang, Ioannis Mitliagkas, Christopher Re

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X

June 16, 2016 by hgpu

Analysis and Parameter Prediction of Compiler Transformation for Graphics Processors

Alberto Magni

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Tags: ATI, ATI Radeon HD 5970, ATI Radeon HD 7970, Computer science, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 480, OpenCL, Package, performance portability, Tesla K20, Thesis

June 9, 2016 by hgpu

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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high performance computing on graphics processing units: hgpu.org

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

IODA: an Input/Output Deep Architecture for image labeling

OpenFace: A general-purpose face recognition library with mobile applications

Using Deep Convolutional Neural Networks in Monte Carlo Tree Search

Deep Learning for Mortgage Risk

DeepBE: Learning Deep Binary Encoding for Multi-Label Classification

Persistent RNNs: Stashing Recurrent Weights On-Chip

CNNLab: a Novel Parallel Framework for Neural Networks using GPU and FPGA-a Practical Study with Trade-off Analysis

YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights

cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL

Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs

Analysis and Parameter Prediction of Compiler Transformation for Graphics Processors

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HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

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Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

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