Unleashing the power of edge-cloud generative ai in mobile networks: A survey of aigc services
Artificial Intelligence-Generated Content (AIGC) is an automated method for generating,
manipulating, and modifying valuable and diverse data using AI algorithms creatively. This …
manipulating, and modifying valuable and diverse data using AI algorithms creatively. This …
Edge computing and its role in Industrial Internet: Methodologies, applications, and future directions
Abstract Proliferation of Industrial Internet has dramatically changed the way we live and
work. It brings convenience to our society and sometimes requires real-time processing of …
work. It brings convenience to our society and sometimes requires real-time processing of …
Computation offloading and task scheduling for DNN-based applications in cloud-edge computing
Due to the high demands of deep neural network (DNN) based applications on
computational capability, it is hard for them to be directly run on mobile devices with limited …
computational capability, it is hard for them to be directly run on mobile devices with limited …
Optimizing Inference Distribution for Efficient Kidney Tumor Segmentation Using a UNet-PWP Deep-Learning Model with XAI on CT Scan Images
Kidney tumors represent a significant medical challenge, characterized by their often-
asymptomatic nature and the need for early detection to facilitate timely and effective …
asymptomatic nature and the need for early detection to facilitate timely and effective …
An intelligent collaborative inference approach of service partitioning and task offloading for deep learning based service in mobile edge computing networks
X Li, Y Qin, H Zhou, Z Zhang - Transactions on Emerging …, 2021 - Wiley Online Library
As the rapid evolution of smart devices and real‐time applications, many new kinds of
computation‐intensive services have been emerged successively and the corresponding …
computation‐intensive services have been emerged successively and the corresponding …
AI: Distributed Inference with Local Edge Devices and Minimal Latency
We propose Edge^n AI, a framework to decompose a complex deep neural networks (DNN)
over n available local edge devices with minimal communication overhead and overall …
over n available local edge devices with minimal communication overhead and overall …
SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models
The deployment of Deep Neural Network (DNN)-based networks on resource-constrained
devices remains a significant challenge due to their high computational and parameter …
devices remains a significant challenge due to their high computational and parameter …
DISTINQT: A Distributed Privacy Aware Learning Framework for QoS Prediction for Future Mobile and Wireless Networks
Beyond 5G and 6G networks are expected to support new and challenging use cases and
applications that depend on a certain level of Quality of Service (QoS) to operate smoothly …
applications that depend on a certain level of Quality of Service (QoS) to operate smoothly …
[HTML][HTML] Layer-wise partitioning and merging for efficient and scalable deep learning
SB Akintoye, L Han, H Lloyd, X Zhang… - Future Generation …, 2023 - Elsevier
Abstract Deep Neural Network (DNN) models are usually trained sequentially from one layer
to another, which causes forward, backward and update locking problems, leading to poor …
to another, which causes forward, backward and update locking problems, leading to poor …
Scenario-aware program specialization for timing predictability
J Benz, O Bringmann - ACM Transactions on Architecture and Code …, 2021 - dl.acm.org
The successful application of static program analysis strongly depends on flow facts of a
program such as loop bounds, control-flow constraints, and operating modes. This problem …
program such as loop bounds, control-flow constraints, and operating modes. This problem …