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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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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. ...
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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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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 ...
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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...
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06/22/2018
Deploying Deep Neural Networks in the Embedded Space
Recently, Deep Neural Networks (DNNs) have emerged as the dominant model...
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05/22/2018
CascadeCNN: Pushing the performance limits of quantisation
This work presents CascadeCNN, an automated toolflow that pushes the qua...
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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...
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01/07/2018