We introduce an analytical system model based on credible real-world measurements to capture the end-to-end consumption of ML schemes.

Abstract—The convergence of Machine Learning (ML) with the edge computing paradigm has paved the way for distributing processing-heavy ML tasks to the ...

Jul 7, 2021 · PDF | The convergence of Machine Learning (ML) with the edge computing paradigm has paved the way for distributing processing-heavy ML tasks ...

Sep 30, 2023 · Bibliographic details on On the Resource Consumption of Distributed ML.

This study presents Driple that designs graph neural networks to predict the resource consumption of diverse workloads.

Jul 12, 2021 · In this context, we employ an edge-based (EL) and a federated (FL) ML scheme and in-depth compare their bandwidth needs and energy footprint ...

On the Resource Consumption of Distributed ML. Georgios Drainakis, Panagiotis Pantazopoulos, Konstantinos V. Katsaros, Vasilis Sourlas, Angelos Amditis.

4 days ago · In this context, research is needed to tailor AI/ML mechanisms to edge computing to securely harvest heterogeneous resources including computing ...

Abstract:Deep learning (DL) workflows demand an ever-increasing budget of compute and energy in order to achieve outsized gains.

ABSTRACT. Elasticity—scaling out or in depending upon resource demand or availability—allows a system to improve its efficiency or performance.