Mario Almeida | DeepAI

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∙ 12/15/2022

NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution

In recent years, image and video delivery systems have begun integrating...

0 Stylianos I. Venieris, et al. ∙

∙ 09/28/2021

Smart at what cost? Characterising Mobile Deep Neural Networks in the wild

With smartphones' omnipresence in people's pockets, Machine Learning (ML...

0 Mario Almeida, et al. ∙

∙ 04/20/2021

DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device

Recently, there has been an explosive growth of mobile and embedded appl...

0 Mario Almeida, et al. ∙

∙ 02/26/2021

FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout

Federated Learning (FL) has been gaining significant traction across dif...

0 Samuel Horvath, et al. ∙

∙ 08/14/2020

SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud

Despite the soaring use of convolutional neural networks (CNNs) in mobil...

9 Stefanos Laskaridis, et al. ∙

∙ 06/14/2019

Diffusing Your Mobile Apps: Extending In-Network Function Virtualization to Mobile Function Offloading

Motivated by the huge disparity between the limited battery capacity of ...

0 Mario Almeida, et al. ∙

∙ 05/17/2019

EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices

In recent years, advances in deep learning have resulted in unprecedente...

5 Mario Almeida, et al. ∙

∙ 03/09/2018

A Family of Droids: Analyzing Behavioral Model based Android Malware Detection via Static and Dynamic Analysis

As smartphones play an increasingly central role in our everyday lives, ...

0 Lucky Onwuzurike, et al. ∙

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