16 citations · 30 across the 8 of their papers we have counts for
17 papers
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).…
Low-Cost On-device Partial Domain Adaptation (LoCO-PDA): Enabling efficient CNN retraining on edge devices
Aditya Rajagopal, Christos-Savvas Bouganis
With the increased deployment of Convolutional Neural Networks (CNNs) on edge devices, the uncertainty of the observed data distribution upon deployment has led researchers to to u…
perf4sight: A toolflow to model CNN training performance on Edge GPUs
Aditya Rajagopal, Christos-Savvas Bouganis
The increased memory and processing capabilities of today's edge devices create opportunities for greater edge intelligence. In the domain of vision, the ability to adapt a Convolu…
Performance landscape of resource-constrained platforms targeting DNNs
Panagiotis Miliadis, Christos-Savvas Bouganis, Dionisios Pnevmatikatos
Over the recent years, a significant number of complex, deep neural networks have been developed for a variety of applications including speech and face recognition, computer visio…
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…