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PipeCNN: An OpenCL-Based FPGA Accelerator for Large-Scale Convolution Neuron Networks

Dong Wang, Jianjing An, Ke Xu

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Tags: CNN, Computer science, Deep learning, DSP, FPGA, Hardware Architecture, Neural networks, OpenCL, Package

November 10, 2016 by hgpu

Tamp: A Library for Compact Deep Neural Networks with Structured Matrices

Bingchen Gong, Brendan Jou, Felix Yu, Shih-Fu Chang

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Tags: Caffe, Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, TensorFlow

November 8, 2016 by hgpu

Accelerate Deep Learning Inference with MCTS in the game of Go on the Intel Xeon Phi

Ching-Nung Lin, Shi-Jim Yen

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Tags: Computer science, Deep learning, Intel Xeon Phi, Neural networks

November 8, 2016 by hgpu

Molecular Activity Prediction using Deep Learning Software Library

Yoshiki Kato, Shinji Hamada, Hitoshi Goto

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Tags: Bioinformatics, Chemistry, Computer science, CUDA, Deep learning, nVidia, Package, Python, Tesla K20

November 5, 2016 by hgpu

Extensions and Limitations of the Neural GPU

Eric Price, Wojciech Zaremba, Ilya Sutskever

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Tags: Algorithms, Computer science, CUDA, Deep learning, nVidia, Package, Python

November 3, 2016 by hgpu

Classification Performance of Convolutional Neural Networks

Niklas Mattsson

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Tags: Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 960, nVidia Tegra TX1, Performance, Python, Theano, Thesis

November 1, 2016 by hgpu

LightRNN: Memory and Computation-Efficient Recurrent Neural Networks

Xiang Li, Tao Qin, Jian Yang, Tie-Yan Liu

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Tags: Algorithms, Computer science, Deep learning, Neural networks, NLP, nVidia, RNN, Tesla K20

November 1, 2016 by hgpu

GPflow: A Gaussian process library using TensorFlow

Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo Leon-Villagra, Zoubin Ghahramani, James Hensman

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan X, Package, Python, TensorFlow

October 29, 2016 by hgpu

Machine Learning Based Intrusion Detection in Controller Area Networks

Roar Elias Georgsen

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Tags: Computer science, Deep learning, DSP, FPGA, Machine learning, OpenCL, Package, Python, Security, SoC

October 15, 2016 by hgpu

Neural Network Computing Using On-Chip Accelerators

Schuyler Eldridge

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Tags: Artificial intelligence, Computer science, Deep learning, Machine learning, Neural networks, Package, Thesis

October 12, 2016 by hgpu

Implementation of Frequency Domain Convolution for the Caffe-Framework

Philipp Harzig

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

October 8, 2016 by hgpu

Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks

Roberto DiCecco, Griffin Lacey, Jasmina Vasiljevic, Paul Chow, Graham Taylor, Shawki Areibi

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

October 4, 2016 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

PipeCNN: An OpenCL-Based FPGA Accelerator for Large-Scale Convolution Neuron Networks

Tamp: A Library for Compact Deep Neural Networks with Structured Matrices

Accelerate Deep Learning Inference with MCTS in the game of Go on the Intel Xeon Phi

Molecular Activity Prediction using Deep Learning Software Library

Extensions and Limitations of the Neural GPU

Classification Performance of Convolutional Neural Networks

LightRNN: Memory and Computation-Efficient Recurrent Neural Networks

GPflow: A Gaussian process library using TensorFlow

Machine Learning Based Intrusion Detection in Controller Area Networks

Neural Network Computing Using On-Chip Accelerators

Implementation of Frequency Domain Convolution for the Caffe-Framework

Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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