Deep learning has led to tremendous advancements in the field of Artificial Intelligence. One
caveat, however, is the substantial amount of compute needed to train these deep learning …

Deep learning (DL) applications are increasingly being deployed on HPC systems, to
leverage the massive parallelism and computing power of those systems for DL model …

Large language models (LLMs) represented by chartGPT have achieved profound
applications and breakthroughs in various fields. This demonstrates that LLMs with …

The nondeterminism of Deep Learning (DL) training algorithms and its influence on the
explainability of neural network (NN) models are investigated in this work with the help of …

The dedicated memory of hardware accelerators can be insufficient to store all weights
and/or intermediate states of large deep learning models. Although model parallelism is a …

Deep learning (DL) is becoming increasingly popular in several application domains and
has made several new application features involving computer vision, speech recognition …

Withthe rapiddevelopment of deep learning, the size of data sets anddeepneuralnetworks
(DNNs) models are also booming. As a result, the intolerable long time for models' training …

Recently, distributed systems have become the backbone of technological development. It
serves as the foundation for new trends technologies such as blockchain, the internet of …

Prevalent large CNN models pose a significant challenge in terms of computing resources
for resource-constrained devices at the Edge. Distributing the computations and coefficients …

Traditional machine learning training is a static process that lacks real-time adaptability of
hyperparameters. Popular tuning solutions during runtime involve checkpoints and …