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Found 5 papers, 3 papers with code
DynaSplit: A Hardware-Software Co-Design Framework for Energy-Aware Inference on Edge
While split computing enables the decomposition of large neural networks (NNs) and allows partial computation on both edge and cloud devices, identifying the most suitable split layer and hardware configurations is a non-trivial task.
A Decentralized and Self-Adaptive Approach for Monitoring Volatile Edge Environments
However, traditional monitoring systems have a centralized architecture for both data plane and control plane, which increases latency, creates a failure bottleneck, and faces challenges in providing quick and trustworthy data in volatile edge environments, especially where infrastructures are often built upon failure-prone, unsophisticated computing and network resources.
An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge
In this paper, we present an energy-aware approach for the design and deployment of self-adaptive AI-based applications that can balance application objectives (e. g., accuracy in object detection and frames processing rate) with energy consumption.
Towards Self-Adaptive Peer-to-Peer Monitoring for Fog Environments
In such environments, monitoring solutions have to cope with the heterogeneity of the devices and platforms present in the Fog, the limited resources available at the edge of the network, and the high dynamism of the whole Cloud-to-Thing continuum.
Cloud Failure Prediction with Hierarchical Temporal Memory: An Empirical Assessment
Hierarchical Temporal Memory (HTM) is an unsupervised learning algorithm inspired by the features of the neocortex that can be used to continuously process stream data and detect anomalies, without requiring a large amount of data for training nor requiring labeled data.
