Search Results for author: Alessandro Tundo

Found 5 papers, 3 papers with code

DynaSplit: A Hardware-Software Co-Design Framework for Energy-Aware Inference on Edge

no code implementations31 Oct 2024 Daniel May, Alessandro Tundo, Shashikant Ilager, Ivona Brandic

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.

Scheduling

A Decentralized and Self-Adaptive Approach for Monitoring Volatile Edge Environments

1 code implementation13 May 2024 Shashikant Ilager, Jakob Fahringer, Alessandro Tundo, Ivona Brandić

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.

Edge-computing

An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge

1 code implementation31 Aug 2023 Alessandro Tundo, Marco Mobilio, Shashikant Ilager, Ivona Brandić, Ezio Bartocci, Leonardo Mariani

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.

object-detection Object Detection +1

Towards Self-Adaptive Peer-to-Peer Monitoring for Fog Environments

no code implementations9 May 2022 Vera Colombo, Alessandro Tundo, Michele Ciavotta, Leonardo Mariani

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

1 code implementation6 Oct 2021 Oliviero Riganelli, Paolo Saltarel, Alessandro Tundo, Marco Mobilio, Leonardo Mariani

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.

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