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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 processing all samples and a heavier, higher-accuracy model conditionally refining harder samples to improve accuracy.
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 neural network (DNN) architectures and approaches emerging every few years, driving the field's advancement.
How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures
Collectively, these results highlight the critical need for further exploration as to how the various cross-stack solutions can be best combined in order to bring the latest advances in deep learning close to users, in a robust and efficient manner.
OODIn: An Optimised On-Device Inference Framework for Heterogeneous Mobile Devices
Radical progress in the field of deep learning (DL) has led to unprecedented accuracy in diverse inference tasks.
PerceptionNet: A Deep Convolutional Neural Network for Late Sensor Fusion
Human Activity Recognition (HAR) based on motion sensors has drawn a lot of attention over the last few years, since perceiving the human status enables context-aware applications to adapt their services on users' needs.
