296 citations · 595 across the 9 of their papers we have counts for
17 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…
How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures
Stylianos I. Venieris, Ioannis Panopoulos, Ilias Leontiadis +1
The unprecedented performance of deep neural networks (DNNs) has led to large strides in various Artificial Intelligence (AI) inference tasks, such as object and speech recognition…
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation
Ilias Leontiadis, Stefanos Laskaridis, Stylianos I. Venieris +1
On-device machine learning is becoming a reality thanks to the availability of powerful hardware and model compression techniques. Typically, these models are pretrained on large G…
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…
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments
Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas +4
We present DarkneTZ, a framework that uses an edge device's Trusted Execution Environment (TEE) in conjunction with model partitioning to limit the attack surface against Deep Neur…
A Retrospective Analysis of User Exposure to (Illicit) Cryptocurrency Mining on the Web
Ralph Holz, Diego Perino, Matteo Varvello +6
In late 2017, a sudden proliferation of malicious JavaScript was reported on the Web: browser-based mining exploited the CPU time of website visitors to mine the cryptocurrency Mon…