4 citations · 9 across the 6 of their papers we have counts for
6 papers
Closing the Gap between Client and Global Model Performance in Heterogeneous Federated Learning
Hongrui Shi, Valentin Radu, Po Yang
The heterogeneity of hardware and data is a well-known and studied problem in the community of Federated Learning (FL) as running under heterogeneous settings. Recently, custom-siz…
Data Selection for Efficient Model Update in Federated Learning
Hongrui Shi, Valentin Radu
The Federated Learning (FL) workflow of training a centralized model with distributed data is growing in popularity. However, until recently, this was the realm of contributing cli…
Optimising the Performance of Convolutional Neural Networks across Computing Systems using Transfer Learning
Rik Mulder, Valentin Radu, Christophe Dubach
The choice of convolutional routines (primitives) to implement neural networks has a tremendous impact on their inference performance (execution speed) on a given hardware platform…
TASO: Time and Space Optimization for Memory-Constrained DNN Inference
Yuan Wen, Andrew Anderson, Valentin Radu +2
Convolutional neural networks (CNNs) are used in many embedded applications, from industrial robotics and automation systems to biometric identification on mobile devices. State-of…
CamLoc: Pedestrian Location Detection from Pose Estimation on Resource-constrained Smart-cameras
Adrian Cosma, Ion Emilian Radoi, Valentin Radu
Recent advancements in energy-efficient hardware technology is driving the exponential growth we are experiencing in the Internet of Things (IoT) space, with more pervasive computa…
Distilling with Performance Enhanced Students
Jack Turner, Elliot J. Crowley, Valentin Radu +3
The task of accelerating large neural networks on general purpose hardware has, in recent years, prompted the use of channel pruning to reduce network size. However, the efficacy o…