19 citations · 40 across the 10 of their papers we have counts for
27 papers · 1 filter
NEUKONFIG: Reducing Edge Service Downtime When Repartitioning DNNs
Ayesha Abdul Majeed, Peter Kilpatrick, Ivor Spence +1
Deep Neural Networks (DNNs) may be partitioned across the edge and the cloud to improve the performance efficiency of inference. DNN partitions are determined based on operational…
Revisiting the Arguments for Edge Computing Research
Blesson Varghese, Eyal de Lara, Aaron Ding +8
This article argues that low latency, high bandwidth, device proliferation, sustainable digital infrastructure, and data privacy and sovereignty continue to motivate the need for e…
ScissionLite: Accelerating Distributed Deep Neural Networks Using Transfer Layer
Hyunho Ahn, Munkyu Lee, Cheol-Ho Hong +1
Industrial Internet of Things (IIoT) applications can benefit from leveraging edge computing. For example, applications underpinned by deep neural networks (DNN) models can be slic…
AVEC: Accelerator Virtualization in Cloud-Edge Computing for Deep Learning Libraries
Jason Kennedy, Blesson Varghese, Carlos Reaño
Edge computing offers the distinct advantage of harnessing compute capabilities on resources located at the edge of the network to run workloads of relatively weak user devices. Th…
Scission: Performance-driven and Context-aware Cloud-Edge Distribution of Deep Neural Networks
Luke Lockhart, Paul Harvey, Pierre Imai +2
Partitioning and distributing deep neural networks (DNNs) across end-devices, edge resources and the cloud has a potential twofold advantage: preserving privacy of the input data,…
A Case For Adaptive Deep Neural Networks in Edge Computing
Francis McNamee, Schahram Dustadar, Peter Kilpatrick +3
Edge computing offers an additional layer of compute infrastructure closer to the data source before raw data from privacy-sensitive and performance-critical applications is transf…