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Found 5 papers, 3 papers with code
Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems
Offloading the execution of complex Deep Neural Networks (DNNs) models to compute-capable devices at the network edge, that is, edge servers, can significantly reduce capture-to-output delay.
Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edge Computing Systems
In this paper, we propose to modify the structure and training process of DNN models for complex image classification tasks to achieve in-network compression in the early network layers.
BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing
We show that BottleFit decreases power consumption and latency respectively by up to 49% and 89% with respect to (w. r. t.)
SmartDet: Context-Aware Dynamic Control of Edge Task Offloading for Mobile Object Detection
We extensively evaluate SmartDet on a real-world testbed composed of a JetSon Nano as mobile device and a GTX 980 Ti as edge server, connected through a Wi-Fi link.
The use of Synthetic Data to solve the scalability and data availability problems in Smart City Digital Twins
However, Digital Twins are data intensive and need highly localized data, making them difficult to scale, particularly to small cities, and with the high cost associated to data collection.
