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Tango: A Deep Neural Network Benchmark Suite for Various Accelerators

Aajna Karki, Chethan Palangotu Keshava, Spoorthi Mysore Shivakumar, Joshua Skow, Goutam Madhukeshwar Hegde, Hyeran Jeon

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Tags: Benchmarking, Computer science, CUDA, Deep learning, FPGA, Machine learning, Neural networks, nVidia, nVidia Jetson TX1, OpenCL, Package

January 20, 2019 by hgpu

HG-Caffe: Mobile and Embedded Neural Network GPU (OpenCL) Inference Engine with FP16 Supporting

Zhuoran Ji

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

January 13, 2019 by hgpu

ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers

Ao Ren, Tianyun Zhang, Shaokai Ye, Jiayu Li, Wenyao Xu, Xuehai Qian, Xue Lin, Yanzhi Wang

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Tags: Caffe, Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Package, Tesla P100

January 6, 2019 by hgpu

A Survey on Optimized Implementation of Deep Learning Models on the NVIDIA Jetson Platform

Sparsh Mittal

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia Jetson TK1, nVidia Jetson TX1, nVidia Jetson TX2, OpenCL, survey

December 30, 2018 by hgpu

ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation

Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Hongxu Yin, Fei Sun, Yanghan Wang, Marat Dukhan, Yunqing Hu, Yiming Wu, Yangqing Jia, Peter Vajda, Matt Uyttendaele, Niraj K. Jha

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Tags: Bayesian, Computer science, CUDA, Deep learning, DSP, Neural networks, nVidia, nVidia GeForce GTX 1060

December 30, 2018 by hgpu

wav2letter++: The Fastest Open-source Speech Recognition System

Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Speech recognition, Tesla V100

December 23, 2018 by hgpu

Deep Learning by Doing: The NVIDIA Deep Learning Institute and University Ambassador Program

Xi Chen, Gregory S. Gutmann, Joe Bungo

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Tags: Cloud, Computer science, CUDA, Deep learning, Distributed computing, Education, Neural networks, nVidia, OpenACC

December 23, 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

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks

Linnan Wang, Wei Wu, Yiyang Zhao, Junyu Zhang, Hang Liu, George Bosilca, Jack Dongarra, Maurice Herlihy, Rodrigo Fonseca

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Tags: Compression, Computer science, CUDA, Deep learning, FFT, GPU cluster, Neural networks, nVidia, Package, Tesla P100

November 25, 2018 by hgpu

Modeling Deep Learning Accelerator Enabled GPUs

Md Aamir Raihan, Negar Goli, Tor Aamodt

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Tags: Computer science, CUDA, Deep learning, Hardware Architecture, Machine learning, nVidia, Tesla V100

November 25, 2018 by hgpu

FusionStitching: Deep Fusion and Code Generation for Tensorflow Computations on GPUs

Guoping Long, Jun Yang, Kai Zhu, Wei Lin

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Tags: Code generation, Computer science, CUDA, Deep learning, Machine learning, nVidia, TensorFlow

November 18, 2018 by hgpu

The AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data with GPUs

Ahmad Al Badawi, Jin Chao, Jie Lin, Chan Fook Mun, Sim Jun Jie, Benjamin Hong Meng Tan, Xiao Nan, Khin Mi Mi Aung, Vijay Ramaseshan Chandrasekhar

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Tags: CNN, Computer science, CUDA, Deep learning, nVidia, Security, Tesla V100

November 11, 2018 by hgpu

high performance computing on graphics processing units: hgpu.org

Tango: A Deep Neural Network Benchmark Suite for Various Accelerators

HG-Caffe: Mobile and Embedded Neural Network GPU (OpenCL) Inference Engine with FP16 Supporting

ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers

A Survey on Optimized Implementation of Deep Learning Models on the NVIDIA Jetson Platform

ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation

wav2letter++: The Fastest Open-source Speech Recognition System

A Framework for Fast and Efficient Neural Network Compression

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks

Modeling Deep Learning Accelerator Enabled GPUs

FusionStitching: Deep Fusion and Code Generation for Tensorflow Computations on GPUs

The AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data with GPUs

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