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The Case for Hierarchical Deep Learning Inference at the Network Edge
Depending on the application, if the inference provided by the local algorithm is incorrect or further assistance is required from large DL models on edge or cloud, only then the ED offloads the data sample.
Online Algorithms for Hierarchical Inference in Deep Learning applications at the Edge
In order to get the best out of both worlds, i. e., the benefits of doing inference on the ED and the benefits of doing inference on ES, we explore the idea of Hierarchical Inference (HI), wherein S-ML inference is only accepted when it is correct, otherwise the data sample is offloaded for L-ML inference.
Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge
We propose an efficient NN model management framework, called Edge-MultiAI, that ushers the NN models of the DL applications into the edge memory such that the degree of multi-tenancy and the number of warm-starts are maximized.
Offloading Algorithms for Maximizing Inference Accuracy on Edge Device Under a Time Constraint
With the emergence of edge computing, the problem of offloading jobs between an Edge Device (ED) and an Edge Server (ES) received significant attention in the past.
On the Minimum Achievable Age of Information for General Service-Time Distributions
There has been a significant research effort in optimizing this metric in communication and networking systems under different settings.
