Natural hazards have the potential to cause catastrophic damage and significant
socioeconomic loss. The actual damage and loss observed in the recent decades has …

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 …

Artificial intelligence (AI) is changing fundamentally the way how IT solutions are
implemented and operated across all application domains, including the geospatial domain …

Distributed synchronous stochastic gradient descent (SGD) algorithms are widely used in
large-scale deep learning applications, while it is known that the communication bottleneck …

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 …

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 …

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 …

The plethora of complex Artificial Intelligence (AI) algorithms and available High-
Performance Computing (HPC) power stimulates the expeditious development of AI …

Deep learning and machine learning (ML) technologies have been implemented in various
applications, and various agriculture technologies are being developed based on image …

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 …