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Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs

Hamid Tabani, Fabio Mazzocchetti, Pedro Benedicte, Jaume Abella, Francisco J. Cazorla

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson TX2, Performance

April 25, 2021 by hgpu

AVEC: Accelerator Virtualization in Cloud-Edge Computing for Deep Learning Libraries

Jason Kennedy, Blesson Varghese, Carlos Reaño

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Tags: Cloud, Computer science, CUDA, Deep learning, nVidia, nVidia Jetson TX2, Virtualization

March 21, 2021 by hgpu

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

Walther Carballo-Hernández, Maxime Pelcat, François Berry

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

February 7, 2021 by hgpu

Efficient Deep Neural Network Inference for Embedded Systems: A Mixture of Experts Approach

Benjamin David Taylor

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Tags: Algorithms, Cloud, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia Jetson TX2, Thesis

November 29, 2020 by hgpu

Deep-Edge: An Efficient Framework for Deep Learning Model Update on Heterogeneous Edge

Anirban Bhattacharjee, Ajay Dev Chhokra, Hongyang Sun, Shashank Shekhar, Aniruddha Gokhale, Gabor Karsai, Abhishek Dubey

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Tags: Cloud, Computer science, Computer vision, CUDA, Deep learning, Heterogeneous systems, nVidia, nVidia Jetson TX2, Performance

April 19, 2020 by hgpu

Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

Balazs Nagy, Philipp Foehn, Davide Scaramuzza

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Tags: Algorithms, Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GTX 960 M, nVidia Jetson TX2, OpenCV, Package

April 5, 2020 by hgpu

Interoperable GPU Kernels as Latency Improver for MEC

Juuso Haavisto, Jukka Riekki

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Tags: Computer science, GLSL, Graphics, nVidia, nVidia GeForce RTX 2080, nVidia Jetson TX2, Package, Vulkan

February 2, 2020 by hgpu

Using Parallel Programming Models for Automotive Workloads on Heterogeneous Systems – a Case Study

Lukas Sommer, Florian Stock, Leonardo Solis-Vasquez, Andreas Koch

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Tags: Automotive, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson TX2, OpenCL, OpenMP

January 26, 2020 by hgpu

Benchmarking Deep Learning Models on Jetson TX2

Lucas Pedro Bordignon, Aldo von Wangenheim

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Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, Image recognition, nVidia, nVidia Jetson TX2, Overview, Package, Tutorial

November 24, 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

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

A Power Efficient Neural Network Implementation on Heterogeneous FPGA and GPU Devices

Yuexuan Tu, Saad Sadiq, Yudong Tao, Mei-Ling Shyu, Shu-Ching Chen

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Tags: Computer science, Deep learning, Energy-efficient computing, FPGA, Neural networks, nVidia, nVidia Jetson TX2

July 28, 2019 by hgpu

Face.evoLVe: High-Performance Face Recognition Library based on PyTorch & PaddlePaddle

Face.evoLVe: A High-Performance Face Recognition Library

OpenCLDecompiler

A method for decompilation of AMD GCN kernels to OpenCL

PULP: a (Parallel Ultra-Low-Power) open-source multi-core computing platform

DNN is not all you need: Parallelizing Non-Neural ML Algorithms on Ultra-Low-Power IoT Processors

StreamBlocks: A compiler for heterogeneous dataflow computing

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

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The 18th International Conference on High Performance Computing & Simulation (HPCS), 2020

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24-27

Naha, Okinawa, Japan

The Fifth International Workshop on GPU Computing and AI (GCA), 2020

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13-15

Shenzhen, China

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Bangkok, Thailand

International Conference on Cyber Physical Systems and IoT (CPSIOT’20), 2020

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high performance computing on graphics processing units: hgpu.org

Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs

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

Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

Interoperable GPU Kernels as Latency Improver for MEC

Using Parallel Programming Models for Automotive Workloads on Heterogeneous Systems – a Case Study

Benchmarking Deep Learning Models on Jetson TX2

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

Survey and Benchmarking of Machine Learning Accelerators

A Power Efficient Neural Network Implementation on Heterogeneous FPGA and GPU Devices

Recent source codes

Face.evoLVe: High-Performance Face Recognition Library based on PyTorch & PaddlePaddle

OpenCLDecompiler

PULP: a (Parallel Ultra-Low-Power) open-source multi-core computing platform

StreamBlocks: A compiler for heterogeneous dataflow computing

Bempp-cl: a boundary element method library

futhark-stencil-benchmarks

POCL: Portable Computing Language

K-FAC: PyTorch Distributed K-FAC Preconditioner

pystencils

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

Most viewed papers (last 30 days)

Featured events

9th International Workshop on OpenCL and SYCL, 2021

The 18th International Conference on High Performance Computing & Simulation (HPCS), 2020

The Fifth International Workshop on GPU Computing and AI (GCA), 2020

2nd International Conference on Frontiers of Intelligent Manufacturing and Automation (CFIMA’21), 2021

International Conference on Cyber Physical Systems and IoT (CPSIOT’20), 2020