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Found 16 papers, 7 papers with code
Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization
We develop Reconvene, a system for rapidly generating pruned models suited for edge deployments using structured PaI.
DRIVE: Dual Gradient-Based Rapid Iterative Pruning
Pruning methods, such as iterative magnitude-based pruning (IMP) achieve up to a 90% parameter reduction while retaining accuracy comparable to the original model.
NeuroFlux: Memory-Efficient CNN Training Using Adaptive Local Learning
NeuroFlux segments a CNN into blocks based on GPU memory usage and further attaches an auxiliary network to each layer in these blocks.
DNNShifter: An Efficient DNN Pruning System for Edge Computing
Compared to sparse models, the pruned model variants are up to 5. 14x smaller and have a 1. 67x inference latency speedup, with no compromise to sparse model accuracy.
ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy
This paper analyzes the effects of dynamically varying video contents and detection latency on the real-time detection accuracy of a detector and proposes a new run-time accuracy variation model, ROMA, based on the findings from the analysis.
CONTINUER: Maintaining Distributed DNN Services During Edge Failures
This paper will leverage trade-offs in accuracy, end-to-end latency and downtime for selecting the best technique given user-defined objectives (accuracy, latency and downtime thresholds) when an edge node fails.
Roadmap for Edge AI: A Dagstuhl Perspective
Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI.
FedFly: Towards Migration in Edge-based Distributed Federated Learning
Our empirical results on the CIFAR10 dataset, with both balanced and imbalanced data distribution, support our claims that FedFly can reduce training time by up to 33% when a device moves after 50% of the training is completed, and by up to 45% when 90% of the training is completed when compared to state-of-the-art offloading approach in FL.
FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning
Further, FedAdapt adopts reinforcement learning based optimization and clustering to adaptively identify which layers of the DNN should be offloaded for each individual device on to a server to tackle the challenges of computational heterogeneity and changing network bandwidth.
ScissionLite: Accelerating Distributed Deep Neural Networks Using Transfer Layer
For example, applications underpinned by deep neural networks (DNN) models can be sliced and distributed across the IIoT device and the edge of the network for improving the overall performance of inference and for enhancing privacy of the input data, such as industrial product images.
AVEC: Accelerator Virtualization in Cloud-Edge Computing for Deep Learning Libraries
This is achieved by offloading computationally intensive workloads, such as deep learning from user devices to the edge.
Edge-computing
Distributed, Parallel, and Cluster Computing
Networking and Internet Architecture
Scission: Performance-driven and Context-aware Cloud-Edge Distribution of Deep Neural Networks
This is because the combination of potential target hardware resources that maximizes performance and the sequence of layers of the DNN that should be distributed across the target resources needs to be determined, while accounting for user-defined objectives/constraints for partitioning.
A Case For Adaptive Deep Neural Networks in Edge Computing
However, there is limited understanding of: (a) whether and how evolving operational conditions (increased CPU and memory utilization at the edge or reduced data transfer rates between the edge and the cloud) affect the performance of already deployed DNNs, and (b) whether a new partition configuration is required to maximize performance.
DYVERSE: DYnamic VERtical Scaling in Multi-tenant Edge Environments
When compared to executing applications on the Edge servers without dynamic vertical scaling, static priorities and dynamic priorities reduce SLO violation rates of requests by up to 4% and 12% for the online game, respectively, and in both cases 6% for the face detection workload.
Distributed, Parallel, and Cluster Computing Systems and Control
Challenges and Opportunities in Edge Computing
Many cloud-based applications employ a data centre as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables.
Distributed, Parallel, and Cluster Computing
The Royal Birth of 2013: Analysing and Visualising Public Sentiment in the UK Using Twitter
To this end, a framework for analysing and visualising public sentiment from a Twitter corpus is developed.
