296 citations · 777 across the 23 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…