4 citations · 8 across the 11 of their papers we have counts for
7 papers · 1 filter
Intermittent Opportunistic Routing Components for the INET Framework
Edward Longman, Mohammed El-Hajjar, Geoff V. Merrett
Intermittently-powered wireless sensor networks (WSNs) use energy harvesting and small energy storage to remove the need for battery replacement and to extend the operational lifet…
GhostShiftAddNet: More Features from Energy-Efficient Operations
Jia Bi, Jonathon Hare, Geoff V. Merrett
Deep convolutional neural networks (CNNs) are computationally and memory intensive. In CNNs, intensive multiplication can have resource implications that may challenge the ability…
Dynamic Transformer for Efficient Machine Translation on Embedded Devices
Hishan Parry, Lei Xun, Amin Sabet +3
The Transformer architecture is widely used for machine translation tasks. However, its resource-intensive nature makes it challenging to implement on constrained embedded devices,…
Temporal Early Exits for Efficient Video Object Detection
Amin Sabet, Jonathon Hare, Bashir Al-Hashimi +1
Transferring image-based object detectors to the domain of video remains challenging under resource constraints. Previous efforts utilised optical flow to allow unchanged features…
Dynamic-OFA: Runtime DNN Architecture Switching for Performance Scaling on Heterogeneous Embedded Platforms
Wei Lou, Lei Xun, Amin Sabet +3
Mobile and embedded platforms are increasingly required to efficiently execute computationally demanding DNNs across heterogeneous processing elements. At runtime, the available ha…
Optimising Resource Management for Embedded Machine Learning
Lei Xun, Long Tran-Thanh, Bashir M Al-Hashimi +1
Machine learning inference is increasingly being executed locally on mobile and embedded platforms, due to the clear advantages in latency, privacy and connectivity. In this paper,…
Incremental Training and Group Convolution Pruning for Runtime DNN Performance Scaling on Heterogeneous Embedded Platforms
Lei Xun, Long Tran-Thanh, Bashir M Al-Hashimi +1
Inference for Deep Neural Networks is increasingly being executed locally on mobile and embedded platforms due to its advantages in latency, privacy and connectivity. Since modern…