296 citations · 477 across the 8 of their papers we have counts for
8 papers
Smart at what cost? Characterising Mobile Deep Neural Networks in the wild
Mario Almeida, Stefanos Laskaridis, Abhinav Mehrotra +3
With smartphones' omnipresence in people's pockets, Machine Learning (ML) on mobile is gaining traction as devices become more powerful. With applications ranging from visual filte…
DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device
Mario Almeida, Stefanos Laskaridis, Stylianos I. Venieris +2
Recently, there has been an explosive growth of mobile and embedded applications using convolutional neural networks(CNNs). To alleviate their excessive computational demands, deve…
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
Samuel Horvath, Stefanos Laskaridis, Mario Almeida +3
Federated Learning (FL) has been gaining significant traction across different ML tasks, ranging from vision to keyboard predictions. In large-scale deployments, client heterogenei…
SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud
Stefanos Laskaridis, Stylianos I. Venieris, Mario Almeida +2
Despite the soaring use of convolutional neural networks (CNNs) in mobile applications, uniformly sustaining high-performance inference on mobile has been elusive due to the excess…
Diffusing Your Mobile Apps: Extending In-Network Function Virtualization to Mobile Function Offloading
Mario Almeida, Liang Wang, Jeremy Blackburn +2
Motivated by the huge disparity between the limited battery capacity of user devices and the ever-growing energy demands of modern mobile apps, we propose INFv. It is the first off…
EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
Mario Almeida, Stefanos Laskaridis, Ilias Leontiadis +2
In recent years, advances in deep learning have resulted in unprecedented leaps in diverse tasks spanning from speech and object recognition to context awareness and health monitor…
A Family of Droids -- Android Malware Detection via Behavioral Modeling: Static vs Dynamic Analysis
Lucky Onwuzurike, Mario Almeida, Enrico Mariconti +3
Following the increasing popularity of mobile ecosystems, cybercriminals have increasingly targeted them, designing and distributing malicious apps that steal information or cause…
C3PO: Computation Congestion Control (PrOactive) - an algorithm for dynamic diffusion of ephemeral in-network services
Liang Wang, Mario Almeida, Jeremy Blackburn +1
There is an obvious trend that more and more data and computation are migrating into networks nowadays. Combining mature virtualization technologies with service-centric net- worki…