high performance computing on graphics processing units: hgpu.org

hgpu.org » Neural networks

Computational Performance Predictions for Deep Neural Network Training: A Runtime-Based Approach

Geoffrey X. Yu, Yubo Gao, Pavel Golikov, Gennady Pekhimenko

View

Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce RTX 2070, Performance, Python, Tesla P100, Tesla V100

February 7, 2021 by hgpu

Why is FPGA-GPU Heterogeneity the Best Option for Embedded Deep Neural Networks?

Walther Carballo-Hernández, Maxime Pelcat, François Berry

View

Download (PDF)

Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Neural networks, nVidia, nVidia Jetson TX2, Performance

February 7, 2021 by hgpu

Implementation of Autoencoders with Systolic Arrays through OpenCL

Rafael Gadea-Gironés, Vicente Herrero-Bosch, Jose Monzó-Ferrer, Ricardo Colom-Paleros

View

Download (PDF)

Tags: Algorithms, Computer science, Deep learning, FPGA, HLS, Neural networks, OpenCL

January 17, 2021 by hgpu

Fast convolutional neural networks on FPGAs with hls4ml

Thea Aarrestad, Vladimir Loncar, Maurizio Pierini, Sioni Summers, Jennifer Ngadiuba, Christoffer Petersson, Hampus Linander, Yutaro Iiyama, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Dylan Rankin, Sergo Jindariani, Kevin Pedro, Nhan Tran, Mia Liu, Edward Kreinar, Zhenbin Wu, Duc Hoang

View

Download (PDF)

Source codes

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

January 17, 2021 by hgpu

I/O Lower Bounds for Auto-tuning of Convolutions in CNNs

Xiaoyang Zhang, Junmin Xiao, Guangming Tan

View

Download (PDF)

Tags: Auto-Tuning, CNN, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Tesla V100

January 3, 2021 by hgpu

When Machine Learning Meets Quantum Computers: A Case Study

Weiwen Jiang, Jinjun Xiong, Yiyu Shi

View

Download (PDF)

Tags: Computer science, CUDA, FPGA, Machine learning, Neural networks, nVidia, Quantum computing

December 27, 2020 by hgpu

Solving Mixed Integer Programs Using Neural Networks

Vinod Nair, Sergey Bartunov, Felix Gimeno, Ingrid von Glehn, Pawel Lichocki, Ivan Lobov, Brendan O'Donoghue, Nicolas Sonnerat, Christian Tjandraatmadja, Pengming Wang, Ravichandra Addanki, Tharindi Hapuarachchi, Thomas Keck, James Keeling, Pushmeet Kohli, Ira Ktena, Yujia Li, Oriol Vinyals, Yori Zwols

View

Download (PDF)

Tags: Computer science, Deep learning, Machine learning, Mathematics, Neural networks, nVidia

December 27, 2020 by hgpu

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

Maurizio Capra, Beatrice Bussolino, Alberto Marchisio, Guido Masera, Maurizio Martina, Muhammad Shafique

View

Download (PDF)

Tags: Artificial intelligence, Benchmarking, Computer science, Computer vision, CUDA, Deep learning, Machine learning, Medicine, Neural networks, nVidia, Tesla V100

December 27, 2020 by hgpu

CNN2Gate: An Implementation of Convolutional Neural Networks Inference on FPGAs with Automated Design Space Exploration

Alireza Ghaffari, Yvon Savaria

View

Download (PDF)

Tags: Computer science, Design space exploration, FPGA, Machine learning, Neural networks, OpenCL

December 27, 2020 by hgpu

NVIDIA SimNet: an AI-accelerated multi-physics simulation framework

Oliver Hennigh, Susheela Narasimhan, Mohammad Amin Nabian, Akshay Subramaniam, Kaustubh Tangsali, Max Rietmann, Jose del Aguila Ferrandis, Wonmin Byeon, Zhiwei Fang, Sanjay Choudhry

View

Download (PDF)

Tags: cfd, CUDA, Fluid dynamics, Linear Algebra, Machine learning, Neural networks, nVidia, nVidia A100, Partial differential equations, PDEs, Physics, Tesla V100, Video