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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

Parallel LZ77 Decoding using a GPU

Emmanuel Morfiadakis

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 840 M, Thesis

September 23, 2018 by hgpu

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Jack Turner, Jose Cano, Valentin Radu, Elliot J. Crowley, Michael O'Boyle, Amos Storkey

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Tags: ARM, Compression, Computer science, Deep learning, Machine learning, Neural networks, OpenCL, OpenMP, Package

September 16, 2018 by hgpu

Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

Wei Tan, Shiyu Chang, Liana Fong, Cheng Li, Zijun Wang, Liangliang Cao

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Tags: Algorithms, Compression, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX Titan X, Package

August 19, 2018 by hgpu

Beyond Straightforward Vectorization of Lightweight Data Compression Algorithms for Larger Vector Sizes

Johannes Pietrzyk, Annett Ungethum, Dirk Habich, Wolfgang Lehner

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Tags: Algorithms, Compression, Computer science, Intel Xeon Phi

July 5, 2018 by hgpu

Energy Efficient Computing on Multi-core Processors: Vectorization and Compression Techniques

Abdullah Al Hasib

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Tags: Compression, Computer science, Data parallelism, Intel Xeon Phi, Prefetch, Programming techniques, Thesis

June 20, 2018 by hgpu

Neural Multi-scale Image Compression

Ken Nakanishi, Shin-ichi Maeda, Takeru Miyato, Daisuke Okanohara

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Tags: Compression, Computer vision, CUDA, Deep learning, Image processing, Machine learning, nVidia

May 20, 2018 by hgpu

Design of Hardware Accelerator for Lempel-Ziv 4 (LZ4) Compression

Sang Muk Lee, Ji Hoon Jang, Jung Hwan Oh, Ji Kwang Kim, Seung Eun Lee

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Tags: Algorithms, Compression, Computer science, FPGA

May 22, 2017 by hgpu

Real-Time Adaptive Image Compression

Oren Rippel, Lubomir Bourdev

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Tags: Algorithms, Compression, Computer science, Computer vision, Machine learning, nVidia, nVidia GeForce GTX 980 Ti

May 18, 2017 by hgpu

Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks

Minsoo Rhu, Mike O'Connor, Niladrish Chatterjee, Jeff Pool, Stephen W. Keckler

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

May 6, 2017 by hgpu

Faster across the PCIe bus: A GPU library for lightweight decompression

Eyal Rozenberg, Peter Boncz

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Package

April 15, 2017 by hgpu

OpenDwarfs: a benchmark suite consisting of different computation/communication idioms

Exploring FPGA-specific Optimizations for Irregular OpenCL Applications

Tango: A Deep Neural Network Benchmark Suite for Various Accelerators

Tango: A Deep Neural Network Benchmark Suite for Various Accelerators

MapD Core database

Exact Selectivity Computation for Modern In-Memory Database Query Optimization

BitCracker: the first open source password cracking tool for memory units encrypted with BitLocker

BitCracker: BitLocker meets GPUs

caffe-android-opencl-fp16: Optimised Caffe with OpenCL supporting for less powerful devices such as mobile phones

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

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

AutoParallel-Fortran: A domain specific, automatically parallelising source-to-source compiler for Fortran-95 that takes scientific Fortran as input and produces parallel Fortran/OpenCL

Towards Automatic Transformation of Legacy Scientific Code into OpenCL for Optimal Performance on FPGAs

gpussjoin: Set Similarity joins in the GPGPU paradigm

Speeding-up the Verification Phase of Set Similarity Joins in the GPGPU paradigm

wav2letter: Facebook AI Research Automatic Speech Recognition Toolkit

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

GenASiS_Basics: Object-oriented Utilitarian Functionality for Large-scale Physics Simulations

Targeting GPUs with OpenMP Directives on Summit: A Simple and Effective Fortran Experience

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2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

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