:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Ping, Jared M., Nixon, Ken J.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2403.05106
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

TinyML for Acoustic Anomaly Detection in IoT Sensor Networks
by: Almaini, Amar, et al.
Published: (2026)

Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML
by: Deutel, Mark, et al.
Published: (2023)

Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure
by: Dehrouyeh, Fatemeh, et al.
Published: (2025)

TinySV: Speaker Verification in TinyML with On-device Learning
by: Pavan, Massimo, et al.
Published: (2024)

Fully Autonomous Z-Score-Based TinyML Anomaly Detection on Resource-Constrained MCUs Using Power Side-Channel Data
by: Albaiz, Abdulrahman, et al.
Published: (2026)

K-Means Based TinyML Anomaly Detection and Distributed Model Reuse via the Distributed Internet of Learning (DIoL)
by: Albaiz, Abdulrahman, et al.
Published: (2026)

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices
by: Suwannaphong, Thanaphon, et al.
Published: (2024)

On-device Online Learning and Semantic Management of TinyML Systems
by: Ren, Haoyu, et al.
Published: (2024)

TinyML-Enabled IoT for Sustainable Precision Irrigation
by: Taueatsoala, Kamogelo, et al.
Published: (2026)

MLonMCU: TinyML Benchmarking with Fast Retargeting
by: van Kempen, Philipp, et al.
Published: (2023)

TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems
by: Huckelberry, Jacob, et al.
Published: (2024)

MicroFlow: An Efficient Rust-Based Inference Engine for TinyML
by: Carnelos, Matteo, et al.
Published: (2024)

On The Dynamic Ensemble Selection for TinyML-based Systems -- a Preliminary Study
by: Puslecki, Tobiasz, et al.
Published: (2025)

U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT
by: Huang, Zhaolan, et al.
Published: (2023)

Accelerating TinyML Inference on Microcontrollers through Approximate Kernels
by: Armeniakos, Giorgos, et al.
Published: (2024)

Dendron: Enhancing Human Activity Recognition with On-Device TinyML Learning
by: Shalby, Hazem Hesham Yousef, et al.
Published: (2025)

A TinyML Reinforcement Learning Approach for Energy-Efficient Light Control in Low-Cost Greenhouse Systems
by: Salem, Mohamed Abdallah, et al.
Published: (2025)

Once-for-All Channel Mixers (HYPERTINYPW): Generative Compression for TinyML
by: Shaalan, Yassien
Published: (2026)

DEBUG-HD: Debugging TinyML models on-device using Hyper-Dimensional computing
by: Ghanathe, Nikhil P, et al.
Published: (2024)

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference
by: Woodward, Kieran, et al.
Published: (2025)

Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?
by: Zeinaty, Christophe El, et al.
Published: (2025)

Hybrid Convolution and Vision Transformer NAS Search Space for TinyML Image Classification
by: Djajapermana, Mikhael, et al.
Published: (2025)

What changes after deployment? A survey on On-device Learning in TinyML
by: Pavan, Massimo, et al.
Published: (2026)

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications
by: Leslin, Jelin, et al.
Published: (2025)

Securing Radiation Detection Systems with an Efficient TinyML-Based IDS for Edge Devices
by: Pizarro, Einstein Rivas, et al.
Published: (2025)

On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case
by: Dehrouyeh, Fatemeh, et al.
Published: (2024)

DTMM: Deploying TinyML Models on Extremely Weak IoT Devices with Pruning
by: Han, Lixiang, et al.
Published: (2024)

Benchmarking Energy and Latency in TinyML: A Novel Method for Resource-Constrained AI
by: Bartoli, Pietro, et al.
Published: (2025)

Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learning
by: Bao, Ziyuan, et al.
Published: (2025)

Integration of TinyML and LargeML: A Survey of 6G and Beyond
by: Vu, Thai-Hoc, et al.
Published: (2025)

Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification
by: Eldeeb, Eslam, et al.
Published: (2024)

QUTE: Quantifying Uncertainty in TinyML with Early-exit-assisted ensembles for model-monitoring
by: Ghanathe, Nikhil P, et al.
Published: (2024)

Consolidating TinyML Lifecycle with Large Language Models: Reality, Illusion, or Opportunity?
by: Wu, Guanghan, et al.
Published: (2025)

TinyTNAS: GPU-Free, Time-Bound, Hardware-Aware Neural Architecture Search for TinyML Time Series Classification
by: Saha, Bidyut, et al.
Published: (2024)

Optimizing TinyML: The Impact of Reduced Data Acquisition Rates for Time Series Classification on Microcontrollers
by: Samanta, Riya, et al.
Published: (2024)

A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management
by: Sucipto, Willy, et al.
Published: (2025)

Toward Attention-based TinyML: A Heterogeneous Accelerated Architecture and Automated Deployment Flow
by: Wiese, Philip, et al.
Published: (2024)

RISC-V Based TinyML Accelerator for Depthwise Separable Convolutions in Edge AI
by: Yildirim, Muhammed, et al.
Published: (2025)

msf-CNN: Patch-based Multi-Stage Fusion with Convolutional Neural Networks for TinyML
by: Huang, Zhaolan, et al.
Published: (2025)

Real-Time Performance Benchmarking of TinyML Models in Embedded Systems (PICO: Performance of Inference, CPU, and Operations)
by: Dey, Abhishek, et al.
Published: (2025)