research
∙
07/25/2023
Mitigating Memory Wall Effects in CNN Engines with On-the-Fly Weights Generation
The unprecedented accuracy of convolutional neural networks (CNNs) acros...
share
research
∙
07/19/2023
TinyTrain: Deep Neural Network Training at the Extreme Edge
On-device training is essential for user personalisation and privacy. Wi...
research
∙
06/22/2023
MultiTASC: A Multi-Tenancy-Aware Scheduler for Cascaded DNN Inference at the Consumer Edge
Cascade systems comprise a two-model sequence, with a lightweight model ...
research
∙
06/20/2023
Exploring the Performance and Efficiency of Transformer Models for NLP on Mobile Devices
Deep learning (DL) is characterised by its dynamic nature, with new deep...
research
∙
12/15/2022
NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution
In recent years, image and video delivery systems have begun integrating...
research
∙
12/15/2022
Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation
Recent image degradation estimation methods have enabled single-image su...
research
∙
10/19/2022
The Future of Consumer Edge-AI Computing
Deep Learning has proliferated dramatically across consumer devices in l...
research
∙
09/27/2022
Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs
With deep neural networks (DNNs) emerging as the backbone in a multitude...
research
∙
09/20/2022
Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design
Attention-based neural networks have become pervasive in many AI tasks. ...
research
∙
05/19/2022
Multi-DNN Accelerators for Next-Generation AI Systems
As the use of AI-powered applications widens across multiple domains, so...
research
∙
06/21/2021
How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures
The unprecedented performance of deep neural networks (DNNs) has led to ...
research
∙
06/08/2021
OODIn: An Optimised On-Device Inference Framework for Heterogeneous Mobile Devices
Radical progress in the field of deep learning (DL) has led to unprecede...
research
∙
06/07/2021
Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions
Internet-enabled smartphones and ultra-wide displays are transforming a ...
research
∙
06/07/2021
Multi-Exit Semantic Segmentation Networks
Semantic segmentation arises as the backbone of many vision systems, spa...
research
∙
04/20/2021
DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device
Recently, there has been an explosive growth of mobile and embedded appl...
research
∙
03/09/2021
unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generation
Single computation engines have become a popular design choice for FPGA-...
research
∙
02/26/2021
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
Federated Learning (FL) has been gaining significant traction across dif...
research
∙
02/02/2021
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation
On-device machine learning is becoming a reality thanks to the availabil...
research
∙
10/12/2020
Neural Enhancement in Content Delivery Systems: The State-of-the-Art and Future Directions
Internet-enabled smartphones and ultra-wide displays are transforming a ...
research
∙
08/14/2020
SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud
Despite the soaring use of convolutional neural networks (CNNs) in mobil...
research
∙
08/10/2020
HAPI: Hardware-Aware Progressive Inference
Convolutional neural networks (CNNs) have recently become the state-of-t...
research
∙
07/08/2020
Journey Towards Tiny Perceptual Super-Resolution
Recent works in single-image perceptual super-resolution (SR) have demon...
research
∙
06/18/2020
Caffe Barista: Brewing Caffe with FPGAs in the Training Loop
As the complexity of deep learning (DL) models increases, their compute ...
research
∙
06/16/2020
Multi-Precision Policy Enforced Training (MuPPET): A precision-switching strategy for quantised fixed-point training of CNNs
Large-scale convolutional neural networks (CNNs) suffer from very long t...
research
∙
08/21/2019
MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors
In recent years, convolutional networks have demonstrated unprecedented ...
research
∙
05/17/2019
EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
In recent years, advances in deep learning have resulted in unprecedente...
research
∙
05/02/2019
Approximate LSTMs for Time-Constrained Inference: Enabling Fast Reaction in Self-Driving Cars
The need to recognise long-term dependencies in sequential data such as ...
research
∙
07/13/2018
CascadeCNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks
This work presents CascadeCNN, an automated toolflow that pushes the qua...
research
∙
06/22/2018
Deploying Deep Neural Networks in the Embedded Space
Recently, Deep Neural Networks (DNNs) have emerged as the dominant model...
research
∙
05/25/2018
f-CNN^x: A Toolflow for Mapping Multiple Convolutional Neural Networks on FPGAs
The predictive power of Convolutional Neural Networks (CNNs) has been an...
research
∙
05/22/2018
CascadeCNN: Pushing the performance limits of quantisation
This work presents CascadeCNN, an automated toolflow that pushes the qua...
research
∙
03/15/2018
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
In the past decade, Convolutional Neural Networks (CNNs) have demonstrat...
research
∙
01/07/2018
Approximate FPGA-based LSTMs under Computation Time Constraints
Recurrent Neural Networks and in particular Long Short-Term Memory (LSTM...
research
∙
11/23/2017