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09/13/2023
DNNShifter: An Efficient DNN Pruning System for Edge Computing
Deep neural networks (DNNs) underpin many machine learning applications....
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08/31/2023
MS-BioGraphs: Sequence Similarity Graph Datasets
Progress in High-Performance Computing in general, and High-Performance ...
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04/11/2023
Communication Efficient DNN Partitioning-based Federated Learning
Efficiently running federated learning (FL) on resource-constrained devi...
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11/02/2021
FedFly: Towards Migration in Edge-based Distributed Federated Learning
Federated learning (FL) is a privacy-preserving distributed machine lear...
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07/09/2021
FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning
Applying Federated Learning (FL) on Internet-of-Things devices is necess...
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06/29/2021
NEUKONFIG: Reducing Edge Service Downtime When Repartitioning DNNs
Deep Neural Networks (DNNs) may be partitioned across the edge and the c...
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08/04/2020
A Case For Adaptive Deep Neural Networks in Edge Computing
Edge computing offers an additional layer of compute infrastructure clos...
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03/17/2020
Cross Architectural Power Modelling
Existing power modelling research focuses on the model rather than the p...
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02/12/2020
Modelling Fog Offloading Performance
Fog computing has emerged as a computing paradigm aimed at addressing th...
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09/11/2019
Performance Estimation of Container-Based Cloud-to-Fog Offloading
Fog computing offloads latency critical application services running on ...
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11/11/2018
RADS: Real-time Anomaly Detection System for Cloud Data Centres
Cybersecurity attacks in Cloud data centres are increasing alongside the...
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10/27/2017