high performance computing on graphics processing units: hgpu.org

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

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high performance computing on graphics processing units: hgpu.org

AccUDNN: A GPU Memory Efficient Accelerator for Training Ultra-deep Deep Neural Networks

AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning

A Survey of FPGA Based Deep Learning Accelerators: Challenges and Opportunities

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

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