4 citations · 4 across the 6 of their papers we have counts for
4 papers · 1 filter
CARIn: Constraint-Aware and Responsive Inference on Heterogeneous Devices for Single- and Multi-DNN Workloads
Ioannis Panopoulos, Stylianos I. Venieris, Iakovos S. Venieris
The relentless expansion of deep learning applications in recent years has prompted a pivotal shift toward on-device execution, driven by the urgent need for real-time processing,…
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…
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…
OODIn: An Optimised On-Device Inference Framework for Heterogeneous Mobile Devices
Stylianos I. Venieris, Ioannis Panopoulos, Iakovos S. Venieris
Radical progress in the field of deep learning (DL) has led to unprecedented accuracy in diverse inference tasks. As such, deploying DL models across mobile platforms is vital to e…