1 citations · 2 across the 5 of their papers we have counts for
6 papers
MANE: A Multi-Path Adaptive Network for Edge Onloading of Deep Neural Networks
Sokratis Nikolaidis, Stylianos I. Venieris, Leonidas Malachias +1
Split computing constitutes a widely used distributed inference approach, where a lightweight head model is onloaded onto the device and a heavier tail model resides on an edge ser…
A-THENA: Early Intrusion Detection for IoT with Time-Aware Hybrid Encoding and Network-Specific Augmentation
Ioannis Panopoulos, Maria Lamprini A. Bartsioka, Sokratis Nikolaidis +3
The proliferation of Internet of Things (IoT) devices has significantly expanded attack surfaces, making IoT ecosystems particularly susceptible to sophisticated cyber threats. To…
Dynamic Temporal Positional Encodings for Early Intrusion Detection in IoT
Ioannis Panopoulos, Maria-Lamprini A. Bartsioka, Sokratis Nikolaidis +3
The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges, necessitating efficient and adaptive Intrusion Detection Systems (IDS). Traditio…
MultiTASC++: A Continuously Adaptive Scheduler for Edge-Based Multi-Device Cascade Inference
Sokratis Nikolaidis, Stylianos I. Venieris, Iakovos S. Venieris
Cascade systems, consisting of a lightweight model processing all samples and a heavier, high-accuracy model refining challenging samples, have become a widely-adopted distributed…
MultiTASC: A Multi-Tenancy-Aware Scheduler for Cascaded DNN Inference at the Consumer Edge
Sokratis Nikolaidis, Stylianos I. Venieris, Iakovos S. Venieris
Cascade systems comprise a two-model sequence, with a lightweight model processing all samples and a heavier, higher-accuracy model conditionally refining harder samples to improve…
Exploring the Performance and Efficiency of Transformer Models for NLP on Mobile Devices
Ioannis Panopoulos, Sokratis Nikolaidis, Stylianos I. Venieris +1
Deep learning (DL) is characterised by its dynamic nature, with new deep neural network (DNN) architectures and approaches emerging every few years, driving the field's advancement…