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perf4sight: A toolflow to model CNN training performance on Edge GPUs

Aditya Rajagopal, Christos-Savvas Bouganis

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Tags: AI, Computer science, CUDA, Neural networks, nVidia, nVidia Jetson TX2, Package, Performance, Tesla P40

August 22, 2021 by hgpu

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

GCN Inference Acceleration HLS

GCN Inference Acceleration using High-Level Synthesis

Climate Machine: an Earth System Model that automatically learns from data

Large-eddy simulations with ClimateMachine: a new open-source code for atmospheric simulations on GPUs and CPUs

HLS Portability from Intel to Xilinx: A Case Study

HEXL: Intel Homomorphic Encryption Acceleration Library

Accelerating Encrypted Computing on Intel GPUs

Taskflow: A General-purpose Parallel and Heterogeneous Task Programming System

Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System

CompilerGym: a library performant reinforcement learning environments for compiler tasks

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

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high performance computing on graphics processing units: hgpu.org

perf4sight: A toolflow to model CNN training performance on Edge GPUs

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

Recent source codes

GCN Inference Acceleration HLS

Climate Machine: an Earth System Model that automatically learns from data

HLS Portability from Intel to Xilinx: A Case Study

HEXL: Intel Homomorphic Encryption Acceleration Library

Taskflow: A General-purpose Parallel and Heterogeneous Task Programming System

CompilerGym: a library performant reinforcement learning environments for compiler tasks

HPCToolkit

NVPTX-SPIR-V Translator

Taichi Lattice Boltmann

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

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