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Real world applications of Artificial Intelligence on constrained hardware

Wiebe Van Ranst

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Tags: Artificial intelligence, Computer science, Computer vision, Image processing, nVidia, nVidia Jetson TX2, OpenCL, Thesis

April 20, 2019 by hgpu

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

Giang Nguyen, Stefan Dlugolinsky, Martin Bobak, Viet Tran, Alvaro Lopez Garcia, Ignacio Heredia, Peter Malik, Ladislav Hluchy

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Data mining, Deep learning, Machine learning, nVidia, OpenCL

March 31, 2019 by hgpu

Performance Optimization of Memory Intensive Applications on FPGA Accelerator

Arslan Arif

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Tags: Artificial intelligence, Computer science, Electronic design automation, FPGA, nVidia, nVidia GeForce GTX 960, OpenCL, Thesis

March 17, 2019 by hgpu

DNNVM: End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-based CNN Accelerators

Yu Xing, Shuang Liang, Lingzhi Sui, Xijie Jia, Jiantao Qiu, Xin Liu, Yushun Wang, Yu Wang, Yi Shan

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Tags: Artificial intelligence, Caffe, Compilers, Computer science, Deep learning, FPGA, Neural networks, TensorFlow

February 24, 2019 by hgpu

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wonyong Sung

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Tags: Artificial intelligence, Computer science, Deep learning, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce GTX Titan XP, nVidia Tesla GP100

November 11, 2018 by hgpu

A Survey of FPGA-based Accelerators for Convolutional Neural Networks

Sparsh Mittal

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

October 21, 2018 by hgpu

AI Benchmark: Running Deep Neural Networks on Android Smartphones

Andrey Ignatov, Radu Timofte, William Chou, Ke Wang, Max Wu, Tim Hartley, Luc Van Gool

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Tags: Android, Artificial intelligence, Benchmarking, Computer science, Deep learning, Neural networks

October 21, 2018 by hgpu

On Reinforcement Learning for Full-length Game of StarCraft

Zhen-Jia Pang, Ruo-Ze Liu, Zhou-Yu Meng, Yi Zhang, Yang Yu, Tong Lu

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Tags: AI, Algorithms, Artificial intelligence, Computer science, Deep learning, Games, Machine learning, nVidia, Tesla K40

October 6, 2018 by hgpu

A deep learning approach to autonomous lunar landing

Ilaria Bloise, Marcello Orlandelli

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Tags: Algorithms, Artificial intelligence, Computer science, Deep learning, LSTM, Machine learning, Neural networks, nVidia, RNN, Tesla K20, Thesis

September 16, 2018 by hgpu

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

Francisco M. Castro, Nicolas Guil, Manuel J. Marin-Jimenez, Jesus Perez-Serrano, Manuel Ujaldon

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Tags: Artificial intelligence, CNN, Computer science, Computer vision, CUDA, Deep learning, Energy-efficient computing, Neural networks, nVidia, nVidia GeForce GTX Titan X

August 5, 2018 by hgpu

Fast Locality Sensitive Hashing for Beam Search on GPU

Xing Shi, Shizhen Xu, Kevin Knight

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Data Structures and Algorithms, Hashing, NLP, nVidia, Tesla K20

June 9, 2018 by hgpu

NengoDL: Combining deep learning and neuromorphic modelling methods

Daniel Rasmussen

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Source codes

Tags: Artificial intelligence, Benchmarking, Computer science, Deep learning, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Python

June 2, 2018 by hgpu

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

Real world applications of Artificial Intelligence on constrained hardware

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

Performance Optimization of Memory Intensive Applications on FPGA Accelerator

DNNVM: End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-based CNN Accelerators

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

A Survey of FPGA-based Accelerators for Convolutional Neural Networks

AI Benchmark: Running Deep Neural Networks on Android Smartphones

On Reinforcement Learning for Full-length Game of StarCraft

A deep learning approach to autonomous lunar landing

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

Fast Locality Sensitive Hashing for Beam Search on GPU

NengoDL: Combining deep learning and neuromorphic modelling methods

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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