Applications of artificial intelligence for disaster management
Natural hazards have the potential to cause catastrophic damage and significant
socioeconomic loss. The actual damage and loss observed in the recent decades has …
socioeconomic loss. The actual damage and loss observed in the recent decades has …
Communication-efficient distributed deep learning: A comprehensive survey
Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …
Geospatial artificial intelligence: potentials of machine learning for 3D point clouds and geospatial digital twins
J Döllner - PFG–Journal of Photogrammetry, Remote Sensing and …, 2020 - Springer
Artificial intelligence (AI) is changing fundamentally the way how IT solutions are
implemented and operated across all application domains, including the geospatial domain …
implemented and operated across all application domains, including the geospatial domain …
Communication-efficient distributed deep learning with merged gradient sparsification on GPUs
Distributed synchronous stochastic gradient descent (SGD) algorithms are widely used in
large-scale deep learning applications, while it is known that the communication bottleneck …
large-scale deep learning applications, while it is known that the communication bottleneck …
Benchmarking the performance and energy efficiency of AI accelerators for AI training
Deep learning has become widely used in complex AI applications. Yet, training a deep
neural network (DNNs) model requires a considerable amount of calculations, long running …
neural network (DNNs) model requires a considerable amount of calculations, long running …
Enabling compute-communication overlap in distributed deep learning training platforms
Deep Learning (DL) training platforms are built by interconnecting multiple DL accelerators
(eg, GPU/TPU) via fast, customized interconnects with 100s of gigabytes (GBs) of bandwidth …
(eg, GPU/TPU) via fast, customized interconnects with 100s of gigabytes (GBs) of bandwidth …
Lcrm: Layer-wise complexity reduction method for cnn model optimization on end devices
The increasing significance of state-of-the-art convolutional neural network (CNN) models in
computer vision tasks has led to their widespread use in industry and academia. However …
computer vision tasks has led to their widespread use in industry and academia. However …
AIPerf: Automated machine learning as an AI-HPC benchmark
The plethora of complex Artificial Intelligence (AI) algorithms and available High-
Performance Computing (HPC) power stimulates the expeditious development of AI …
Performance Computing (HPC) power stimulates the expeditious development of AI …
Machine learning feature extraction based on binary pixel quantification using low-resolution images for application of unmanned ground vehicles in apple orchards
HK Lyu, S Yun, B Choi - Agronomy, 2020 - mdpi.com
Deep learning and machine learning (ML) technologies have been implemented in various
applications, and various agriculture technologies are being developed based on image …
applications, and various agriculture technologies are being developed based on image …
GPGPU performance estimation for frequency scaling using cross-benchmarking
Dynamic Voltage and Frequency Scaling (D VFS) on General-Purpose Graphics Processing
Units (GPGPUs) is now becoming one of the most significant techniques to balance …
Units (GPGPUs) is now becoming one of the most significant techniques to balance …