research
∙
11/07/2022
Closing the Gap between Client and Global Model Performance in Heterogeneous Federated Learning
The heterogeneity of hardware and data is a well-known and studied probl...
share
research
∙
11/05/2021
Data Selection for Efficient Model Update in Federated Learning
The Federated Learning workflow of training a centralized model with dis...
research
∙
10/20/2020
Optimising the Performance of Convolutional Neural Networks across Computing Systems using Transfer Learning
The choice of convolutional routines (primitives) to implement neural ne...
research
∙
05/21/2020
TASO: Time and Space Optimization for Memory-Constrained DNN Inference
Convolutional neural networks (CNNs) are used in many embedded applicati...
research
∙
02/20/2020
Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs
Convolutional Neural Networks (CNN) are becoming a common presence in ma...
research
∙
12/28/2018
CamLoc: Pedestrian Location Detection from Pose Estimation on Resource-constrained Smart-cameras
Recent advancements in energy-efficient hardware technology is driving t...
research
∙
10/24/2018
HAKD: Hardware Aware Knowledge Distillation
Despite recent developments, deploying deep neural networks on resource ...
research
∙
10/24/2018
Distilling with Performance Enhanced Students
The task of accelerating large neural networks on general purpose hardwa...
research
∙
09/19/2018
