Efficient multitask learning on resource-constrained systems
We present Antler, which exploits the affinity between all pairs of tasks in a multitask
inference system to construct a compact graph representation of the task set and finds an …
inference system to construct a compact graph representation of the task set and finds an …
PROS: an efficient pattern-driven compressive sensing framework for low-power biopotential-based wearables with on-chip intelligence
While the global healthcare market of wearable devices has been growing significantly in
recent years and is predicted to reach $60 billion by 2028, many important healthcare …
recent years and is predicted to reach $60 billion by 2028, many important healthcare …
LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms
Continual Learning (CL) allows applications such as user personalization and household
robots to learn on the fly and adapt to context. This is an important feature when context …
robots to learn on the fly and adapt to context. This is an important feature when context …
DTMM: Deploying TinyML Models on Extremely Weak IoT Devices with Pruning
L Han, Z Xiao, Z Li - arXiv preprint arXiv:2401.09068, 2024 - arxiv.org
DTMM is a library designed for efficient deployment and execution of machine learning
models on weak IoT devices such as microcontroller units (MCUs). The motivation for …
models on weak IoT devices such as microcontroller units (MCUs). The motivation for …
Unlocking the Non-deterministic Computing Power with Memory-Elastic Multi-Exit Neural Networks
With the increasing demand for Web of Things (WoT) and edge computing, the efficient
utilization of limited computing power on edge devices is becoming a crucial challenge …
utilization of limited computing power on edge devices is becoming a crucial challenge …
Industrial Anomaly Detection on Textures: Multilabel Classification Using MCUs
AT Neto, H São Mamede, VD dos Santos - Procedia Computer Science, 2024 - Elsevier
Anomaly detection in the industrial context, identifying defective products and their
categorization, is a prevalent task. It is aimed to acknowledge if training and testing …
categorization, is a prevalent task. It is aimed to acknowledge if training and testing …
Survey and Enhancements on Deploying LSTM Recurrent Neural Networks on Embedded Systems
G Abib, F Castel, N Satouri, H Afifi… - ICC 2023-IEEE …, 2023 - ieeexplore.ieee.org
The real implementation of a recurrent neural network (RNN) in a low complexity IoT device
is evaluated in order to predict the time series of power consumption in tertiary buildings …
is evaluated in order to predict the time series of power consumption in tertiary buildings …
Scaling Up Task Execution on Resource-Constrained Systems
Y Luo - 2023 - search.proquest.com
The ubiquity of executing machine learning tasks on embedded systems with constrained
resources has made efficient execution of neural networks on these systems under the CPU …
resources has made efficient execution of neural networks on these systems under the CPU …
TinyMM: Multimodal-Multitask Machine Learning on Low-Power MCUs for Smart Glasses
L Demagh, P Garda, C Gilbert… - 2023 IEEE …, 2023 - ieeexplore.ieee.org
Maintaining a sufficient battery life-time is crucial for an everyday wearable instrumented
frame. Therefore, the use of a low-power integrated circuit, such as a microcontroller unit …
frame. Therefore, the use of a low-power integrated circuit, such as a microcontroller unit …
[PDF][PDF] Third Year Report
YD Kwon - 2023 - theyoungkwon.github.io
2. Background. This chapter describes the relevant research in more details in the areas of
on-device ML and CL to discuss the necessity, novelty, and contributions of this thesis. 3 …
on-device ML and CL to discuss the necessity, novelty, and contributions of this thesis. 3 …