296 citations · 809 across the 24 of their papers we have counts for
27 papers
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…
Multi-DNN Accelerators for Next-Generation AI Systems
Stylianos I. Venieris, Christos-Savvas Bouganis, Nicholas D. Lane
As the use of AI-powered applications widens across multiple domains, so do increase the computational demands. Primary driver of AI technology are the deep neural networks (DNNs).…
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…
Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions
Royson Lee, Stylianos I. Venieris, Nicholas D. Lane
Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual apps spanning from on-demand movies and 360° videos to video-conferencing and live streami…
Multi-Exit Semantic Segmentation Networks
Alexandros Kouris, Stylianos I. Venieris, Stefanos Laskaridis +1
Semantic segmentation arises as the backbone of many vision systems, spanning from self-driving cars and robot navigation to augmented reality and teleconferencing. Frequently oper…
unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generation
Stylianos I. Venieris, Javier Fernandez-Marques, Nicholas D. Lane
Single computation engines have become a popular design choice for FPGA-based convolutional neural networks (CNNs) enabling the deployment of diverse models without fabric reconfig…
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…
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…
Neural Enhancement in Content Delivery Systems: The State-of-the-Art and Future Directions
Royson Lee, Stylianos I. Venieris, Nicholas D. Lane
Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual apps spanning from on-demand movies and 360-degree videos to video-conferencing and live s…
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…
HAPI: Hardware-Aware Progressive Inference
Stefanos Laskaridis, Stylianos I. Venieris, Hyeji Kim +1
Convolutional neural networks (CNNs) have recently become the state-of-the-art in a diversity of AI tasks. Despite their popularity, CNN inference still comes at a high computation…
Journey Towards Tiny Perceptual Super-Resolution
Royson Lee, Łukasz Dudziak, Mohamed Abdelfattah +4
Recent works in single-image perceptual super-resolution (SR) have demonstrated unprecedented performance in generating realistic textures by means of deep convolutional networks.…
Caffe Barista: Brewing Caffe with FPGAs in the Training Loop
Diederik Adriaan Vink, Aditya Rajagopal, Stylianos I. Venieris +1
As the complexity of deep learning (DL) models increases, their compute requirements increase accordingly. Deploying a Convolutional Neural Network (CNN) involves two phases: train…
Multi-Precision Policy Enforced Training (MuPPET): A precision-switching strategy for quantised fixed-point training of CNNs
Aditya Rajagopal, Diederik Adriaan Vink, Stylianos I. Venieris +1
Large-scale convolutional neural networks (CNNs) suffer from very long training times, spanning from hours to weeks, limiting the productivity and experimentation of deep learning…
MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors
Royson Lee, Stylianos I. Venieris, Łukasz Dudziak +2
In recent years, convolutional networks have demonstrated unprecedented performance in the image restoration task of super-resolution (SR). SR entails the upscaling of a single low…
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…
Approximate LSTMs for Time-Constrained Inference: Enabling Fast Reaction in Self-Driving Cars
Alexandros Kouris, Stylianos I. Venieris, Michail Rizakis +1
The need to recognise long-term dependencies in sequential data such as video streams has made Long Short-Term Memory (LSTM) networks a prominent Artificial Intelligence model for…
DroNet: Efficient convolutional neural network detector for real-time UAV applications
Christos Kyrkou, George Plastiras, Stylianos Venieris +2
Unmanned Aerial Vehicles (drones) are emerging as a promising technology for both environmental and infrastructure monitoring, with broad use in a plethora of applications. Many su…
CascadeCNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks
Alexandros Kouris, Stylianos I. Venieris, Christos-Savvas Bouganis
This work presents CascadeCNN, an automated toolflow that pushes the quantisation limits of any given CNN model, aiming to perform high-throughput inference. A two-stage architectu…
Deploying Deep Neural Networks in the Embedded Space
Stylianos I. Venieris, Alexandros Kouris, Christos-Savvas Bouganis
Recently, Deep Neural Networks (DNNs) have emerged as the dominant model across various AI applications. In the era of IoT and mobile systems, the efficient deployment of DNNs on e…
CascadeCNN: Pushing the performance limits of quantisation
Alexandros Kouris, Stylianos I. Venieris, Christos-Savvas Bouganis
This work presents CascadeCNN, an automated toolflow that pushes the quantisation limits of any given CNN model, to perform high-throughput inference by exploiting the computation…
f-CNN: A Toolflow for Mapping Multi-CNN Applications on FPGAs
Stylianos I. Venieris, Christos-Savvas Bouganis
The predictive power of Convolutional Neural Networks (CNNs) has been an integral factor for emerging latency-sensitive applications, such as autonomous drones and vehicles. Such s…