high performance computing on graphics processing units: hgpu.org

hgpu.org » Deep learning

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

View

Download (PDF)

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

April 19, 2020 by hgpu

MNN: A Universal and Efficient Inference Engine

Xiaotang Jiang, Huan Wang, Yiliu Chen, Ziqi Wu, Lichuan Wang, Bin Zou, Yafeng Yang, Zongyang Cui, Yu Cai, Tianhang Yu, Chengfei Lv, Zhihua Wu

View

Download (PDF)

Source codes

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

April 12, 2020 by hgpu

Neural Architecture Search for Lightweight Non-Local Networks

Yingwei Li, Xiaojie Jin, Jieru Mei, Xiaochen Lian, Linjie Yang, Cihang Xie, Qihang Yu, Yuyin Zhou, Song Bai, Alan Yuille

View

Download (PDF)

Source codes

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

April 12, 2020 by hgpu

Deep Learning for Compilers

Chris Cummins

View

Download (PDF)

Source codes

Tags: AMD Radeon HD 7970, ATI, Benchmarking, Compilers, Computer science, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX 970, OpenCL, Package, Thesis

April 5, 2020 by hgpu

SOL: Effortless Device Support for AI Frameworks without Source Code Changes

Nicolas Weber, Felipe Huici

View

Download (PDF)

Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan V100, nVidia Quadro P 4000, PyTorch

March 29, 2020 by hgpu

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather

View

Download (PDF)

Source codes

Tags: Computer science, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 970, OpenCL, Package, Performance, Programming Languages

March 29, 2020 by hgpu

Learnergy: Energy-based Machine Learners

Mateus Roder, Gustavo Henrique de Rosa, João Paulo Papa

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, Energy-efficient computing, Machine learning, Neural networks, nVidia, Package, Python, PyTorch

March 22, 2020 by hgpu

Performance evaluation of deep learning on smartphones

Abhishek Srivastava

View

Download (PDF)

Tags: Benchmarking, Computer science, Deep learning, TensorFlow, Thesis

March 22, 2020 by hgpu

ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search

Xu Zhang, Chenjun Zhou, Bo Gu

View

Download (PDF)

Tags: Computer science, Deep learning, Neural networks

March 8, 2020 by hgpu

Accelerating CNN on FPGA: An Implementation of MobileNet on FPGA

Yulan Shen

View

Download (PDF)

Tags: classification, Computer science, Computer vision, Deep learning, FPGA, Image processing, nVidia, nVidia GeForce GTX 940 M, Thesis

March 1, 2020 by hgpu

Performance Counters based Power Modeling of Mobile GPUs using Deep Learning

Nadjib Mammeri, Markus Neu, Sohan Lal, Ben Juurlink

View

Download (PDF)

Tags: Computer science, Deep learning, Energy-efficient computing, Heterogeneous systems, Neural networks, OpenCL, OpenGL, SoC

February 23, 2020 by hgpu

The Deep Learning Compiler: A Comprehensive Survey

Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Depei Qian

View

Download (PDF)

Tags: Compilers, Computer science, Deep learning, Machine learning, Performance

February 16, 2020 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

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

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

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

MNN: A Universal and Efficient Inference Engine

Neural Architecture Search for Lightweight Non-Local Networks

Deep Learning for Compilers

SOL: Effortless Device Support for AI Frameworks without Source Code Changes

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

Learnergy: Energy-based Machine Learners

Performance evaluation of deep learning on smartphones

ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search

Accelerating CNN on FPGA: An Implementation of MobileNet on FPGA

Performance Counters based Power Modeling of Mobile GPUs using Deep Learning

The Deep Learning Compiler: A Comprehensive Survey

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

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

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

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

Most viewed papers (last 30 days)