high performance computing on graphics processing units: hgpu.org

hgpu.org » Neural networks

Deep Learning Based FPGA-CPU Acceleration

R. Luis Jalabert

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Tags: Code generation, Computer science, Deep learning, FPGA, Machine learning, Neural networks, Package, Thesis

November 17, 2019 by hgpu

PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision

Tomasz Kornuta

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Tags: Computer science, Computer vision, Machine learning, Neural networks, Package, Python

October 27, 2019 by hgpu

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

Valentin Radu, Kuba Kaszyk, Yuan Wen, Jack Turner, Jose Cano, Elliot J. Crowley, Bjorn Franke, Amos Storkey, Michael O'Boyle

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Tags: Computer science, CUDA, Neural networks, nVidia, nVidia Jetson TX2, OpenCL

October 13, 2019 by hgpu

MIOpen: An Open Source Library For Deep Learning Primitives

Jehandad Khan, Paul Fultz, Artem Tamazov, Daniel Lowell, Chao Liu, Michael Melesse, Murali Nandhimandalam, Kamil Nasyrov, Ilya Perminov, Tejash Shah, Vasilii Filippov, Jing Zhang, Jing Zhou, Bragadeesh Natarajan, Mayank Daga

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

October 6, 2019 by hgpu

Exascale Deep Learning for Scientific Inverse Problems

Nouamane Laanait, Joshua Romero, Junqi Yin, M. Todd Young, Sean Treichler, Vitalii Starchenko, Albina Borisevich, Alex Sergeev, Michael Matheson

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

September 29, 2019 by hgpu

Performance and Power Evaluation of AI Accelerators for Training Deep Learning Models

Yuxin Wang, Qiang Wang, Shaohuai Shi, Xin He, Zhenheng Tang, Kaiyong Zhao, Xiaowen Chu

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Tags: AI, AMD Radeon R7, ATI, Computer science, Deep learning, Neural networks, nVidia, PyTorch, TensorFlow, Tesla P100, Tesla V100, TPU

September 22, 2019 by hgpu

Neural Network Inference on Mobile SoCs

Siqi Wang, Anuj Pathania, Tulika Mitra

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Tags: ARM, Computer science, Heterogeneous systems, Machine learning, Neural networks, SoC

September 8, 2019 by hgpu

Automated Architecture Design for Deep Neural Networks

Steven Abreu

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Tags: Computer science, Deep learning, GPU cluster, Machine learning, Neural networks, nVidia, Tesla K80, Thesis

September 1, 2019 by hgpu

Survey and Benchmarking of Machine Learning Accelerators

Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, Jeremy Kepner

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Tags: ASIC, Benchmarking, Computer science, CUDA, FPGA, Machine learning, Neural networks, nVidia, nVidia Jetson TX1, nVidia Jetson TX2, Performance, TPU

September 1, 2019 by hgpu

Automatic Compiler Based FPGA Accelerator for CNN Training

Shreyas Kolala Venkataramanaiah, Yufei Ma, Shihui Yin, Eriko Nurvithadhi, Aravind Dasu, Yu Cao, Jae-sun Seo

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Tags: Code generation, Compilers, Computer science, Deep learning, FPGA, Neural networks

August 25, 2019 by hgpu

Mass Estimation from Images using Deep Neural Network and Sparse Ground Truth

Muhammad K.A. Hamdan, Diane Rover, John Just

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

August 18, 2019 by hgpu

A Deep Learning Approach for Automatic Code Optimization in the Tiramisu Compiler

Mohammed Henni, Ilhem Isra Mekki

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Tags: Code generation, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia DGX-1

August 11, 2019 by hgpu

high performance computing on graphics processing units: hgpu.org

Deep Learning Based FPGA-CPU Acceleration

PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

MIOpen: An Open Source Library For Deep Learning Primitives

Exascale Deep Learning for Scientific Inverse Problems

Performance and Power Evaluation of AI Accelerators for Training Deep Learning Models

Neural Network Inference on Mobile SoCs

Automated Architecture Design for Deep Neural Networks

Survey and Benchmarking of Machine Learning Accelerators

Automatic Compiler Based FPGA Accelerator for CNN Training

Mass Estimation from Images using Deep Neural Network and Sparse Ground Truth

A Deep Learning Approach for Automatic Code Optimization in the Tiramisu Compiler

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