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Found 9 papers, 0 papers with code
UR2M: Uncertainty and Resource-Aware Event Detection on Microcontrollers
This limitation hinders the feasibility of many important on-device wearable event detection (WED) applications, such as heart attack detection.
LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms
With respect to the state-of-the-art (SOTA) Meta CL method, LifeLearner drastically reduces the memory footprint (by 178. 7x), end-to-end latency by 80. 8-94. 2%, and energy consumption by 80. 9-94. 2%.
TinyTrain: Deep Neural Network Training at the Extreme Edge
On-device training is essential for user personalisation and privacy.
HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask
Consequently, the training requires high computation cost and a long time to converge while the model performance does not pay off.
YONO: Modeling Multiple Heterogeneous Neural Networks on Microcontrollers
In this paper, we propose YONO, a product quantization (PQ) based approach that compresses multiple heterogeneous models and enables in-memory model execution and switching for dissimilar multi-task learning on MCUs.
Enabling On-Device Smartphone GPU based Training: Lessons Learned
Deep Learning (DL) has shown impressive performance in many mobile applications.
Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applications
Our findings suggest that replay with exemplars-based schemes such as iCaRL has the best performance trade-offs, even in complex scenarios, at the expense of some storage space (few MBs) for training examples (1% to 5%).
FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing Applications
Various incremental learning (IL) approaches have been proposed to help deep learning models learn new tasks/classes continuously without forgetting what was learned previously (i. e., avoid catastrophic forgetting).
Knowing when we do not know: Bayesian continual learning for sensing-based analysis tasks
Our experiments prove the robustness and reliability of the learned models to adapt to the changing sensing environment, and show the suitability of using uncertainty of the predictions to assess their reliability.
