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08/28/2023
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
Deep Neural Networks (DNNs) have been a large driver and enabler for AI ...
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10/19/2022
The Future of Consumer Edge-AI Computing
Deep Learning has proliferated dramatically across consumer devices in l...
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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...
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06/22/2022
FedorAS: Federated Architecture Search under system heterogeneity
Federated learning (FL) has recently gained considerable attention due t...
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09/28/2021
Smart at what cost? Characterising Mobile Deep Neural Networks in the wild
With smartphones' omnipresence in people's pockets, Machine Learning (ML...
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06/09/2021
Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions
DNNs are becoming less and less over-parametrised due to recent advances...
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06/07/2021
Multi-Exit Semantic Segmentation Networks
Semantic segmentation arises as the backbone of many vision systems, spa...
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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...
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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...
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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...
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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...
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08/10/2020
HAPI: Hardware-Aware Progressive Inference
Convolutional neural networks (CNNs) have recently become the state-of-t...
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07/08/2019
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning
End-to-end automatic speech recognition (ASR) models are increasingly la...
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05/17/2019