As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

The advent of machine learning (ML) and Artificial intelligence (AI) has brought about a
significant transformation across multiple industries, as it has facilitated the automation of …

It is a challenging task to train large DNN models on sophisticated GPU platforms with
diversified interconnect capabilities. Recently, pipelined training has been proposed as an …

Autism spectrum disorder (ASD) is highly heterogeneous. Identifying systematic individual
differences in neuroanatomy could inform diagnosis and personalized interventions. The …

Deep Neural Network (DNN) models have continuously been growing in size in order to
improve the accuracy and quality of the models. Moreover, for training of large DNN models …

In training of modern large natural language processing (NLP) models, it has become a
common practice to split models using 3D parallelism to multiple GPUs. Such technique …

This paper proposes CMOS synapse and neuron for use in spiking neural networks to
perform cognitive functions in a bio-inspired manner. The proposed synapse can trace the …

Peridynamics (PD) using integral equations has unique advantages in modeling structural
damage evolution, but the expensive computational and memory costs associated with the …

In the intelligent manufacturing process of solar photovoltaic (PV) cells, the automatic defect
detection system using the Industrial Internet of Things (IIoT) smart cameras and sensors …

We propose XPipe, an efficient asynchronous pipeline model parallelism approach for multi-
GPU DNN training. XPipe is designed to use multiple GPUs to concurrently and …