Image de couverture de livre

Benchmarking Energy and Latency in TinyML: A Novel Method for Resource-Constrained AI

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Bartoli, Pietro, Veronesi, Christian, Giudici, Andrea, Siorpaes, David, Trojaniello, Diana, Zappa, Franco
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Efficient Sensor Fusion for Gesture Recognition on Resource-Constrained Devices
par: Bartoli, Pietro, et autres
Publié: (2026)

Hardware-Aware Neural Feature Extraction for Resource-Constrained Devices
par: Tosini, Francesco, et autres
Publié: (2026)

End-to-End Efficiency in Keyword Spotting: A System-Level Approach for Embedded Microcontrollers
par: Bartoli, Pietro, et autres
Publié: (2025)

TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems
par: Huckelberry, Jacob, et autres
Publié: (2024)

MLonMCU: TinyML Benchmarking with Fast Retargeting
par: van Kempen, Philipp, et autres
Publié: (2023)

TinySV: Speaker Verification in TinyML with On-device Learning
par: Pavan, Massimo, et autres
Publié: (2024)

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications
par: Leslin, Jelin, et autres
Publié: (2025)

TinyML-Enabled IoT for Sustainable Precision Irrigation
par: Taueatsoala, Kamogelo, et autres
Publié: (2026)

TinyML for Acoustic Anomaly Detection in IoT Sensor Networks
par: Almaini, Amar, et autres
Publié: (2026)

Accelerating TinyML Inference on Microcontrollers through Approximate Kernels
par: Armeniakos, Giorgos, et autres
Publié: (2024)

Fully Autonomous Z-Score-Based TinyML Anomaly Detection on Resource-Constrained MCUs Using Power Side-Channel Data
par: Albaiz, Abdulrahman, et autres
Publié: (2026)

Affordable Precision Agriculture: A Deployment-Oriented Review of Low-Cost, Low-Power Edge AI and TinyML for Resource-Constrained Farming Systems
par: Samanta, Riya, et autres
Publié: (2026)

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

Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learning
par: Bao, Ziyuan, et autres
Publié: (2025)

On The Dynamic Ensemble Selection for TinyML-based Systems -- a Preliminary Study
par: Puslecki, Tobiasz, et autres
Publié: (2025)

FERMI-ML: A Flexible and Resource-Efficient Memory-In-Situ SRAM Macro for TinyML acceleration
par: Lokhande, Mukul, et autres
Publié: (2025)

MicroFlow: An Efficient Rust-Based Inference Engine for TinyML
par: Carnelos, Matteo, et autres
Publié: (2024)

Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?
par: Zeinaty, Christophe El, et autres
Publié: (2025)

Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML
par: Deutel, Mark, et autres
Publié: (2023)

Dendron: Enhancing Human Activity Recognition with On-Device TinyML Learning
par: Shalby, Hazem Hesham Yousef, et autres
Publié: (2025)

On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case
par: Dehrouyeh, Fatemeh, et autres
Publié: (2024)

DTMM: Deploying TinyML Models on Extremely Weak IoT Devices with Pruning
par: Han, Lixiang, et autres
Publié: (2024)

Real-Time Performance Benchmarking of TinyML Models in Embedded Systems (PICO: Performance of Inference, CPU, and Operations)
par: Dey, Abhishek, et autres
Publié: (2025)

On-device Online Learning and Semantic Management of TinyML Systems
par: Ren, Haoyu, et autres
Publié: (2024)

U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT
par: Huang, Zhaolan, et autres
Publié: (2023)

Energy Efficient Software Hardware CoDesign for Machine Learning: From TinyML to Large Language Models
par: Vahdatpour, Mohammad Saleh, et autres
Publié: (2026)

TinyTNAS: GPU-Free, Time-Bound, Hardware-Aware Neural Architecture Search for TinyML Time Series Classification
par: Saha, Bidyut, et autres
Publié: (2024)

Optimizing TinyML: The Impact of Reduced Data Acquisition Rates for Time Series Classification on Microcontrollers
par: Samanta, Riya, et autres
Publié: (2024)

BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables
par: Samakovlis, Dimitrios, et autres
Publié: (2024)

Integration of TinyML and LargeML: A Survey of 6G and Beyond
par: Vu, Thai-Hoc, et autres
Publié: (2025)

Consolidating TinyML Lifecycle with Large Language Models: Reality, Illusion, or Opportunity?
par: Wu, Guanghan, et autres
Publié: (2025)

RISC-V Based TinyML Accelerator for Depthwise Separable Convolutions in Edge AI
par: Yildirim, Muhammed, et autres
Publié: (2025)

msf-CNN: Patch-based Multi-Stage Fusion with Convolutional Neural Networks for TinyML
par: Huang, Zhaolan, et autres
Publié: (2025)

A TinyML Reinforcement Learning Approach for Energy-Efficient Light Control in Low-Cost Greenhouse Systems
par: Salem, Mohamed Abdallah, et autres
Publié: (2025)

What changes after deployment? A survey on On-device Learning in TinyML
par: Pavan, Massimo, et autres
Publié: (2026)

A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management
par: Sucipto, Willy, et autres
Publié: (2025)

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices
par: Suwannaphong, Thanaphon, et autres
Publié: (2024)

SNAP-UQ: Self-supervised Next-Activation Prediction for Single-Pass Uncertainty in TinyML
par: Lamaakal, Ismail, et autres
Publié: (2025)

Toward Attention-based TinyML: A Heterogeneous Accelerated Architecture and Automated Deployment Flow
par: Wiese, Philip, et autres
Publié: (2024)

QUTE: Quantifying Uncertainty in TinyML with Early-exit-assisted ensembles for model-monitoring
par: Ghanathe, Nikhil P, et autres
Publié: (2024)