边缘智能 · 全部实体（第 21 / 100 页）

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• Performance Evaluation of Deep Neural Network Models Implemented in MLIR for Microcontrollers
  Η παρούσα διπλωματική εργασία επικεντρώνεται στην ανάπτυξη και τη βελτιστοποίηση βαθιών νευρωνικών δικτύων (Deep Neural Networks - DNNs) για μικροελεγκτές, οι οποίοι χαρακτηρίζονται από αυστηρούς περιορισμούς στη μνήμη και την υπολογιστική ισχύ. Η αυξανόμενη α…
  datacite Μπουζίκας, Γεώργιος Χρ. 2025 置信度 0.66 ΠληροφορικήΝευρονικά ΔύκτιαΜικροελεγκτέςInformaticsNeural Networks
• Hybrid Knowledge Transfer through Attention and Logit Distillation for On-Device Vision Systems in Agricultural IoT
  Integrating deep learning applications into agricultural IoT systems faces a serious challenge of balancing the high accuracy of Vision Transformers (ViTs) with the efficiency demands of resource-constrained edge devices. Large transformer models like the Swin…
  datacite Mugisha, Stanley, Kisitu, Rashid, Tushabe, Florence 2025 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• A Survey on Federated Learning for TinyML: Challenges, Techniques, and Future Directions
  The convergence of Federated Learning (FL) and Tiny Machine Learning (TinyML) represents a transformative step toward enabling intelligent and privacy-preserving applications on resource-constrained edge devices. TinyML focuses on deploying lightweight machine…
  datacite Praveen Kumar Myakala, Prudhvi Naayini, Srikanth Kamatala 2025 置信度 0.66 Federated Learning, TinyML, Distributed Computing, Model Optimization, Communication Efficiency, Privacy Preservation, Resource Constraints, Data Heterogeneity, Security, IoT, Industrial Automation, Edge AI.
• A Survey on Federated Learning for TinyML: Challenges, Techniques, and Future Directions
  The convergence of Federated Learning (FL) and Tiny Machine Learning (TinyML) represents a transformative step toward enabling intelligent and privacy-preserving applications on resource-constrained edge devices. TinyML focuses on deploying lightweight machine…
  datacite Praveen Kumar Myakala, Prudhvi Naayini, Srikanth Kamatala 2025 置信度 0.66 Federated Learning, TinyML, Distributed Computing, Model Optimization, Communication Efficiency, Privacy Preservation, Resource Constraints, Data Heterogeneity, Security, IoT, Industrial Automation, Edge AI.
• Edge Intelligence for Wildlife Conservation: Real-Time Hornbill Call Classification Using TinyML
  Hornbills, an iconic species of Malaysia's biodiversity, face threats from habi-tat loss, poaching, and environmental changes, necessitating accurate and real-time population monitoring that is traditionally challenging and re-source intensive. The emergence o…
  datacite Hing, Kong Ka, Behjati, Mehran 2025 置信度 0.66 Sound (cs.SD)Machine Learning (cs.LG)Audio and Speech Processing (eess.AS)FOS: Computer and information sciencesFOS: Computer and information sciences
• Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?
  This paper introduces a novel framework for designing efficient neural network architectures specifically tailored to tiny machine learning (TinyML) platforms. By leveraging large language models (LLMs) for neural architecture search (NAS), a vision transforme…
  datacite Zeinaty, Christophe El, Hamidouche, Wassim, Herrou, Glenn, Menard, Daniel 等 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Sequential Printed Multilayer Perceptron Circuits for Super-TinyML Multi-Sensory Applications
  Super-TinyML aims to optimize machine learning models for de- ployment on ultra-low-power application domains such as wearable technologies and implants. Such domains also require conformality, flexibility, and non-toxicity which traditional silicon-based sys-…
  datacite Saglam, Gurol, Afentaki, Florentia, Zervakis, Georgios, Tahoori, Mehdi 2025 置信度 0.66
• Micro-AI ecosystem
  Summary: Based on all the PDF I've framed my Micro-AI ecosystem in terms of its impact, structure, and potential to help the world: Micro AI: A Global Force for Local Intelligence 1. What Is Micro AI? Micro AI refers to lightweight, low-resource artificial int…
  datacite Stone, Travis Raymond-Charlie, OpenAI 2025 置信度 0.66
• Micro-AI ecosystem
  Summary: Based on all the PDF I've framed my Micro-AI ecosystem in terms of its impact, structure, and potential to help the world: Micro AI: A Global Force for Local Intelligence 1. What Is Micro AI? Micro AI refers to lightweight, low-resource artificial int…
  datacite Stone, Travis Raymond-Charlie, OpenAI 2025 置信度 0.66
• Consolidating TinyML Lifecycle with Large Language Models: Reality, Illusion, or Opportunity?
  The evolving requirements of Internet of Things (IoT) applications are driving an increasing shift toward bringing intelligence to the edge, enabling real-time insights and decision-making within resource-constrained environments. Tiny Machine Learning (TinyML…
  datacite Wu, Guanghan, Tarkoma, Sasu, Morabito, Roberto 2025 置信度 0.66 Software Engineering (cs.SE)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Advancing Air Quality Monitoring: TinyML-Based Real-Time Ozone Prediction with Cost-Effective Edge Devices
  The escalation of urban air pollution necessitates innovative solutions for real-time air quality monitoring and prediction. This paper introduces a novel TinyML-based system designed to predict ozone concentration in real-time. The system employs an Arduino N…
  datacite Ken, Huam Ming, Behjati, Mehran 2025 置信度 0.66 Signal Processing (eess.SP)Artificial Intelligence (cs.AI)FOS: Electrical engineering, electronic engineering, information engineeringFOS: Electrical engineering, electronic engineering, information engineeringFOS: Computer and information sciences
• Survey Paper on Smart Homes: AI-Enabled Unified Environmental Safety System
  This system utilizes IoT technology to detect gas leaks and monitor air pollution in real-time, combining TinyML and a React Native mobile app. Designed for both residential and industrial applications, the system uses MQ2 and MQ135 sensors to identify gases l…
  datacite Beeta Narayan, Aswathy Rajan, Athira S M, Devika P S 2025 置信度 0.66 IoT, Gas detection, Air pollution monitoring, MQ2, MQ135, AI-powered, TinyML, Edge computing, React Native, NodeMCU, ESP32
• Survey Paper on Smart Homes: AI-Enabled Unified Environmental Safety System
  This system utilizes IoT technology to detect gas leaks and monitor air pollution in real-time, combining TinyML and a React Native mobile app. Designed for both residential and industrial applications, the system uses MQ2 and MQ135 sensors to identify gases l…
  datacite Beeta Narayan, Aswathy Rajan, Athira S M, Devika P S 2025 置信度 0.66 IoT, Gas detection, Air pollution monitoring, MQ2, MQ135, AI-powered, TinyML, Edge computing, React Native, NodeMCU, ESP32
• RIOT-ML: toolkit for over-the-air secure updates and performance evaluation of TinyML models
  Practitioners in the field of TinyML lack so far a comprehensive, “batteries-included” toolkit to streamline continuous integration, continuous deployment and performance assessments of executing diverse machine learning models on various low-power IoT hardwar…
  datacite Huang, Zhaolan, Zandberg, Koen, Schleiser, Kaspar, Baccelli, Emmanuel 2025 置信度 0.66 AIIoTMachine learningLow powerMicrocontroller
• Efficient Continual Learning and On-Device Training for Mobile and IoT Devices
  The surge in mobile phones, wearables, and Internet of Things (IoT) devices has resulted in an abundance of sensor data. This played a pivotal role in the widespread adoption of deep neural networks (DNN) to support various real-world scenarios in mobile compu…
  datacite Kwon, Young Dae 2024 置信度 0.66 Continual LearningEfficient AIFew-Shot LearningIoTMeta-Learning
• Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure
  With the growing need for real-time processing on IoT devices, optimizing machine learning (ML) models' size, latency, and computational efficiency is essential. This paper investigates a pruning method for anomaly detection in resource-constrained environment…
  datacite Dehrouyeh, Fatemeh, Shaer, Ibrahim, Nikan, Soodeh, Ajaei, Firouz Badrkhani 等 2025 置信度 0.66 Machine Learning (cs.LG)Signal Processing (eess.SP)FOS: Computer and information sciencesFOS: Computer and information sciencesFOS: Electrical engineering, electronic engineering, information engineering
• SURVEY ON DEEP LEARNING MODELS ON EDGE DEVICES FOR IOT APPLICATIONS
  ABSTRACT In recent years Deep learning algorithms are used in many applications such as vision recognition, speech recognition, bioinformatics and so on. The Internet of Things is the next booming technology for real-time applications, Augmented reality, Self-…
  datacite Mr. S. MANICKAM, Mr. G .MUTHUPANDI 2024 置信度 0.66 Edge computingDeep learningIoTEmbedded device MLTinyML
• Quantitative Analysis of Deeply Quantized Tiny Neural Networks Robust to Adversarial Attacks
  Reducing the memory footprint of Machine Learning (ML) models, especially Deep Neural Networks (DNNs), is imperative to facilitate their deployment on resource-constrained edge devices. However, a notable drawback of DNN models lies in their susceptibility to …
  datacite Zakariyya, Idris, Ayaz, Ferheen, Kharbouche-Harrari, Mounia, Singer, Jeremy 等 2025 置信度 0.66 Machine Learning (cs.LG)Cryptography and Security (cs.CR)Performance (cs.PF)FOS: Computer and information sciencesFOS: Computer and information sciences
• Management of TinyML Enabled Internet of Things Devices
  datacite Szydlo T, Nagy M 2025 置信度 0.66
• Training on the Fly: On-Device Self-Supervised Learning Aboard Nano-Drones Within 20 mW
  Miniaturized cyber-physical systems (CPSs) powered by tiny machine learning (TinyML), such as nano-drones, are becoming an increasingly attractive technology. Their small form factor (i.e., similar to 10cm diameter) ensures vast applicability, ranging from the…
  datacite Cereda, Elia, Giusti, Alessandro, Palossi, Daniele 2024 置信度 0.66 Embedded MLon-device learningresource-constrained cyber-physical system (CPS)self-supervised learningtiny machine learning (TinyML)
• An Extreme-Edge TCN-Based Low-Latency Collision-Avoidance Safety System for Industrial Machinery
  Modern manufacturing industry relies on complex machinery that requires skills, attention, and precise safety certifications. Protecting operators in the machine's surroundings while at the same time reducing the impact on the normal workflow is a major challe…
  datacite Zanghieri, Marcello, Indirli, Fabrizio, Latella, Antonio, Puglia, Giacomo Michele 等 2024 置信度 0.66 Collision avoidanceembedded systemsincremental learningmicrocontrollerpublic dataset
• Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles
  Standard-sized autonomous vehicles have rapidly improved thanks to the breakthroughs of deep learning. However, scaling autonomous driving to mini-vehicles poses several challenges due to their limited on-board storage and computing capabilities. Moreover, aut…
  datacite de Prado, Miguel, Rusci, Manuele, Capotondi, Alessandro, Donze, Romain 等 2021 置信度 0.66 autonomous drivingtinyMLrobustnessmicro-controllers
• On-device edge learning for IoT data streams: a survey
  This literature review explores continual learning methods for on-device training in the context of neural networks (NNs) and decision trees (DTs) for classification tasks on smart environments. We highlight key constraints, such as data architecture (batch vs…
  datacite Lourenço, Afonso, Rodrigo, João, Gama, João, Marreiros, Goreti 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• In-sensor 24 classes HAR under 850 Bytes
  The year 2023 was a key year for tinyML unleashing a new age of intelligent sensors pushing intelligence from the MCU into the source of the data at the sensor level, enabling them to perform sophisticated algorithms and machine learning models in real-time. T…
  datacite Benmessaoud, Ahmed. S, Kezai, Wassim, Medjani, Farida, Bouaita, Khalid 等 2025 置信度 0.66 Signal Processing (eess.SP)FOS: Electrical engineering, electronic engineering, information engineeringFOS: Electrical engineering, electronic engineering, information engineering
• Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation
  Tiny machine learning (TinyML) promises to revolutionize fields such as healthcare, environmental monitoring, and industrial maintenance by running machine learning models on low-power embedded systems. However, the complex optimizations required for successfu…
  datacite Njor, Emil, Banbury, Colby, Fafoutis, Xenofon 2025 置信度 0.66 Neural and Evolutionary Computing (cs.NE)Artificial Intelligence (cs.AI)Computer Vision and Pattern Recognition (cs.CV)Machine Learning (cs.LG)FOS: Computer and information sciences
• AQ Monitoring for TinyML
  The data in the cloud server “ThingSpeak ”is downloaded inthe form of a CSV file, which can be used to create a data frameusing the ‘Pandas’ module in Python. The aim is to create a model that can predict the quality ofthe air depending on the ppm values detec…
  datacite Dutta, Abir Lal, Mukherjee, Tapajit, Sinha, Jayee 2025 置信度 0.66
• TensorFlores: An Enhanced Python-based TinyML Framework
  The TensorFlores framework is a Python-based solution designed for optimizing machine learning deployment in resource-constrained environments.
  datacite Thommas Kevin Sales Flores, Costa, Daniel Gouveia, Ivanovitch Medeiros Dantas Da Silva 2025 置信度 0.66 CapsuleEngineeringMachine LearningTinyMLEdgeAI
• A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference
  In recent years, the development of smart edge computing systems to process information locally is on the rise. Many near-sensor machine learning (ML) approaches have been implemented to introduce accurate and energy efficient template matching operations in r…
  datacite Woodward, Kieran, Kanjo, Eiman, Papandroulidakis, Georgios, Agwa, Shady 等 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Hardware Architecture (cs.AR)FOS: Computer and information sciencesFOS: Computer and information sciences
• Transformer Deployment on Heterogeneous Many-Core Systems
  datacite Wiese, Philip 2023 置信度 0.66 TransformersDeploymentManycoreTinyMLAccelerator
• Edge AI-Driven Lightweight Intrusion Detection for Underwater IoT Wireless Sensor Networks: Enhancing Adaptability, Efficiency, and Real-Time Security
  Abstract The utilization of Underwater Internet of Things Wireless Sensor Networks (UIoTWSN) is important for the management of resources, monitoring marine environment and conducting environmental evaluations. They dynamic nature of underwater habitats and th…
  datacite S. Arivumani Samson, Dr. M. Nagarajan 2025 置信度 0.66 Edge AI, UIoTWSNs, Intrusion Detection, TinyML, MobileNetV3, GRU, Federated Learning, Energy Efficiency, Blockchain.
• Edge AI-Driven Lightweight Intrusion Detection for Underwater IoT Wireless Sensor Networks: Enhancing Adaptability, Efficiency, and Real-Time Security
  Abstract The utilization of Underwater Internet of Things Wireless Sensor Networks (UIoTWSN) is important for the management of resources, monitoring marine environment and conducting environmental evaluations. They dynamic nature of underwater habitats and th…
  datacite S. Arivumani Samson, Dr. M. Nagarajan 2025 置信度 0.66 Edge AI, UIoTWSNs, Intrusion Detection, TinyML, MobileNetV3, GRU, Federated Learning, Energy Efficiency, Blockchain.
• ETHEREAL: Energy-efficient and High-throughput Inference using Compressed Tsetlin Machine
  The Tsetlin Machine (TM) is a novel alternative to deep neural networks (DNNs). Unlike DNNs, which rely on multi-path arithmetic operations, a TM learns propositional logic patterns from data literals using Tsetlin automata. This fundamental shift from arithme…
  datacite Duan, Shengyu, Shafik, Rishad, Yakovlev, Alex 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Enhancing Field-Oriented Control of Electric Drives with Tiny Neural Network Optimized for Micro-controllers
  The deployment of neural networks on resource-constrained micro-controllers has gained momentum, driving many advancements in Tiny Neural Networks. This paper introduces a tiny feed-forward neural network, TinyFC, integrated into the Field-Oriented Control (FO…
  datacite Elele, Martin Joel Mouk, Pau, Danilo, Zhuang, Shixin, Facchinetti, Tullio 2025 置信度 0.66 Machine Learning (cs.LG)Systems and Control (eess.SY)FOS: Computer and information sciencesFOS: Computer and information sciencesFOS: Electrical engineering, electronic engineering, information engineering
• ViT-Reg: Regression-Focused Hardware-Aware Fine-Tuning for ViT on tinyML Platforms
  Vision Transformers (ViTs) have demonstrated significant improvements in image classification tasks. However, deploying them on resource-constrained tinyML platforms presents considerable challenges due to their high computational demands and dynamic power con…
  datacite Shaharear, Md Ragib, Mazumder, Arnab Neelim, Mohsenin, Tinoosh 2024 置信度 0.66 real-time and energy efficient deploymentComputer visionComputer architectureHardwaretinyML Hardware
• Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learning
  With the growing computational capabilities of microcontroller units (MCUs), edge devices can now support machine learning models. However, deploying decentralised federated learning (DFL) on such devices presents key challenges, including intermittent connect…
  datacite Bao, Ziyuan, Kanjo, Eiman, Banerjee, Soumya, Rashid, Hasib-Al 等 2025 置信度 0.66 Computer Science - Artificial IntelligenceComputer Science - Machine Learning
• Enhanced FIWARE-Based Architecture for Cyber-Physical Systems with tinyML and MLOps: A Case Study on Urban Mobility Systems (paper + code)
  FIWARE Machine Learning TinyML and MLOps - Barrier use case Code to reproduce the use case of paper: @ARTICLE{10754992, author={Conde, Javier and Munoz-Arcentales, Andrés and Alonso, Álvaro and Salvachúa, Joaquín and Huecas, Gabriel}, journal={IT Professional}…
  datacite Javier, Conde, Andrés, Munoz-Arcentales, Alvaro, Alonso, Joaquín, Salvachúa 等 2024 置信度 0.66
• Enhanced FIWARE-Based Architecture for Cyber-Physical Systems with tinyML and MLOps: A Case Study on Urban Mobility Systems (paper + code)
  FIWARE Machine Learning TinyML and MLOps - Barrier use case Code to reproduce the use case of paper: @ARTICLE{10754992, author={Conde, Javier and Munoz-Arcentales, Andrés and Alonso, Álvaro and Salvachúa, Joaquín and Huecas, Gabriel}, journal={IT Professional}…
  datacite Javier, Conde, Andrés, Munoz-Arcentales, Alvaro, Alonso, Joaquín, Salvachúa 等 2024 置信度 0.66
• Phase-Only Fourier Representation for unlocking edge intelligence with Tiny Machine Learning (TinyML)
  The notebook contains the code repository for the manuscript COMMSCHEM-25-0031-T submitted to Communications Chemistry for review. The title of the manuscruipt is "Phase-Only Fourier Representation for unlocking edge intelligence through tiny machine learning …
  datacite Perrotton, Alexandre, Bhattacharya, Abhiroop 2025 置信度 0.66 Deep LearningRaman spectroscopyTinyML
• Phase-Only Fourier Representation for unlocking edge intelligence with Tiny Machine Learning (TinyML)
  The notebook contains the code repository for the manuscript COMMSCHEM-25-0031-T submitted to Communications Chemistry for review. The title of the manuscruipt is "Phase-Only Fourier Representation for unlocking edge intelligence through tiny machine learning …
  datacite Perrotton, Alexandre, Bhattacharya, Abhiroop 2025 置信度 0.66 Deep LearningRaman spectroscopyTinyML
• Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML
  Deploying deep neural networks (DNNs) on microcontrollers (TinyML) is a common trend to process the increasing amount of sensor data generated at the edge, but in practice, resource and latency constraints make it difficult to find optimal DNN candidates. Neur…
  datacite Deutel, Mark, Kontes, Georgios, Mutschler, Christopher, Teich, Jürgen 2023 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• EDGE AI-DRIVEN LIGHTWEIGHT INTRUSION DETECTION FOR UNDERWATER IoT WIRELESS SENSOR NETWORKS: ENHANCING ADAPTABILITY, EFFICIENCY, AND REAL-TIME SECURITY
  Abstract The utilization of Underwater Internet of Things Wireless Sensor Networks (UIoTWSN) is important for the management of resources, monitoring marine environment and conducting environmental evaluations. They dynamic nature of underwater habitats and th…
  datacite S. ARIVUMANI SAMSON, Dr. M. NAGARAJAN 2025 置信度 0.66 Edge AI, UIoTWSNs, Intrusion Detection, TinyML, MobileNetV3, GRU, Federated Learning, Energy Efficiency, Blockchain.
• EDGE AI-DRIVEN LIGHTWEIGHT INTRUSION DETECTION FOR UNDERWATER IoT WIRELESS SENSOR NETWORKS: ENHANCING ADAPTABILITY, EFFICIENCY, AND REAL-TIME SECURITY
  Abstract The utilization of Underwater Internet of Things Wireless Sensor Networks (UIoTWSN) is important for the management of resources, monitoring marine environment and conducting environmental evaluations. They dynamic nature of underwater habitats and th…
  datacite S. ARIVUMANI SAMSON, Dr. M. NAGARAJAN 2025 置信度 0.66 Edge AI, UIoTWSNs, Intrusion Detection, TinyML, MobileNetV3, GRU, Federated Learning, Energy Efficiency, Blockchain.
• Michscan: Black-Box Neural Network Integrity Checking at Runtime Through Power Analysis
  As neural networks are increasingly used for critical decision-making tasks, the threat of integrity attacks, where an adversary maliciously alters a model, has become a significant security and safety concern. These concerns are compounded by the use of licen…
  datacite Paul, Robi, Zuzak, Michael 2025 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• TDMiL: Tiny Distributed Machine Learning for Microcontroller-Based Interconnected Devices
  More and more, edge devices embark Artificial Neuron Networks. In this context, a trend is to simultaneously decentralize their training as much as possible while shrinking their resource requirements, both for inference and training—tasks that are typically i…
  datacite Gulati, Mayank, Zandberg, Koen, Huang, Zhaolan, Wunder, Gerhard 等 2024 置信度 0.66 Distributed learningfederated learning (FL)Internet of Things (IoT)machine learningmicrocontrollers
• Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML (Code)
  This repository contains the Python implementation of the Bayesian optimization-based solver proposed in the work "Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML", including the implementation of the ARS and PPO RL agent…
  datacite Deutel, Mark, Kontes, Georgios, Mutschler, Christopher, Teich, Jürgen 2024 置信度 0.66
• Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML (Code)
  This repository contains the Python implementation of the Bayesian optimization-based solver proposed in the work "Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML", including the implementation of the ARS and PPO RL agent…
  datacite Deutel, Mark, Kontes, Georgios, Mutschler, Christopher, Teich, Jürgen 2024 置信度 0.66
• Toward Attention-based TinyML: A Heterogeneous Accelerated Architecture and Automated Deployment Flow
  One of the challenges for Tiny Machine Learning (tinyML) is keeping up with the evolution of Machine Learning models from Convolutional Neural Networks to Transformers. We address this by leveraging a heterogeneous architectural template coupling RISC-V proces…
  datacite Wiese, Philip, İslamoğlu, Gamze, Scherer, Moritz, Macan, Luka 等 2024 置信度 0.66 Hardware Architecture (cs.AR)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• AI-ANNE: (A) (N)eural (N)et for (E)xploration: Transferring Deep Learning Models onto Microcontrollers and Embedded Systems
  This working paper explores the integration of neural networks onto resource-constrained embedded systems like a Raspberry Pi Pico / Raspberry Pi Pico 2. A TinyML aproach transfers neural networks directly on these microcontrollers, enabling real-time, low-lat…
  datacite Klinkhammer, Dennis 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciencesI.2.5; K.3.2
• MicroFlow: An Efficient Rust-Based Inference Engine for TinyML
  In recent years, there has been a significant interest in developing machine learning algorithms on embedded systems. This is particularly relevant for bare metal devices in Internet of Things, Robotics, and Industrial applications that face limited memory, pr…
  datacite Carnelos, Matteo, Pasti, Francesco, Bellotto, Nicola 2024 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• BandX-Activity: Human Activity Recognition Dataset with Demographics Using the MPU6050 Sensor
  BandX-Activity: Human Activity Recognition Dataset with Demographics Using the MPU6050 Sensor The BandX-Activity dataset is a comprehensive resource for human activity recognition (HAR), collected from 33 volunteers wearing the BandX wristband. This dataset in…
  datacite Saha, Bidyut, Samanta, Riya 2024 置信度 0.66 Human Activity Recognition (HAR)Wearable Electronic DevicesWearable DevicesAccelerometry/statistics & numerical dataAccelerometer Data
• BandX-Activity: Human Activity Recognition Dataset with Demographics Using the MPU6050 Sensor
  BandX-Activity: Human Activity Recognition Dataset with Demographics Using the MPU6050 Sensor The BandX-Activity dataset is a comprehensive resource for human activity recognition (HAR), collected from 33 volunteers wearing the BandX wristband. This dataset in…
  datacite Saha, Bidyut, Samanta, Riya 2024 置信度 0.66 Human Activity Recognition (HAR)Wearable Electronic DevicesWearable DevicesAccelerometry/statistics & numerical dataAccelerometer Data
• Design space exploration of TinyML MLPs on resource constrained FPGAs
  In this master thesis, the genetic algorithm NSGA-II is used in order to generate the optimal solutions for TinyML multilayer perceptrons in regards to accuracy and resources used, implemented on device Zynq-7000. Genetic algorithms are algorithms that are bas…
  datacite Μήτσας, Δημήτριος Νικολάου 2024 置信度 0.66 Εκτίμηση πόρωνΝευρωνικά δίκτυαTinyMLResource estimationNeural networks
• ElectraSight: Smart Glasses with Fully Onboard Non-Invasive Eye Tracking Using Hybrid Contact and Contactless EOG
  Smart glasses with integrated eye tracking technology are revolutionizing diverse fields, from immersive augmented reality experiences to cutting-edge health monitoring solutions. However, traditional eye tracking systems rely heavily on cameras and significan…
  datacite Schärer, Nicolas, Villani, Federico, Melatur, Aishwarya, Peter, Steven 等 2024 置信度 0.66 Signal Processing (eess.SP)FOS: Electrical engineering, electronic engineering, information engineeringFOS: Electrical engineering, electronic engineering, information engineering
• SoloFace: A Single-Face Dataset for Resource-Constrained Face Detection and Tracking
  SoloFace: A Single-Face Dataset for Resource-Constrained Face Detection and Tracking DescriptionSoloFace is a custom dataset derived from the COCO-Faces and Visual Wake Word datasets, specifically designed for single-face detection tasks in resource-constraine…
  datacite Samanta, Riya, Saha, Bidyut 2024 置信度 0.66 TinyMLEmbedded AIResource-Constrained AILow-Power Machine LearningFace Detection
• SoloFace: A Single-Face Dataset for Resource-Constrained Face Detection and Tracking
  SoloFace: A Single-Face Dataset for Resource-Constrained Face Detection and Tracking DescriptionSoloFace is a custom dataset derived from the COCO-Faces and Visual Wake Word datasets, specifically designed for single-face detection tasks in resource-constraine…
  datacite Samanta, Riya, Saha, Bidyut 2024 置信度 0.66 TinyMLEmbedded AIResource-Constrained AILow-Power Machine LearningFace Detection
• Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices
  This paper proposes small and efficient machine learning models (TinyML) for resource-constrained edge devices, specifically for on-device indoor localisation. Typical approaches for indoor localisation rely on centralised remote processing of data transmitted…
  datacite Suwannaphong, Thanaphon, Jovan, Ferdian, Craddock, Ian, McConville, Ryan 2024 置信度 0.66 Machine Learning (cs.LG)Software Engineering (cs.SE)FOS: Computer and information sciencesFOS: Computer and information sciences
• DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators
  Tiny machine learning (TinyML) aims to run ML models on small devices and is increasingly favored for its enhanced privacy, reduced latency, and low cost. Recently, the advent of tiny AI accelerators has revolutionized the TinyML field by significantly enhanci…
  datacite Gong, Taesik, Kawsar, Fahim, Min, Chulhong 2024 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Sequential Printed MLP Circuits for Super TinyML Multi-Sensory Applications
  Super-TinyML aims to optimize machine learning models for deployment on ultra-low-power application domains such as wearable technologies and implants. Such domains also require conformality, flexibility, and non-toxicity which traditional silicon-based system…
  datacite Saglam, Gurol, Afentaki, Florentia, Zervakis, Georgios, Tahoori, Mehdi B. 2024 置信度 0.66 Hardware Architecture (cs.AR)FOS: Computer and information sciencesFOS: Computer and information sciences
• TinyML-Enhanced Detection of Out-of-Distribution Data in Machine Learning Based Control Systems
  datacite Iqbal, Zain, Zamira, Daw, Tullio, Vardanega 2024 置信度 0.66
• TinyML-Enhanced Detection of Out-of-Distribution Data in Machine Learning Based Control Systems
  datacite Iqbal, Zain, Zamira, Daw, Tullio, Vardanega 2024 置信度 0.66
• Out-of-Distribution Detection in Machine Learning Based Control Systems enabled by TinyML
  datacite Iqbal, Zain, Tullio, Vardanega 2024 置信度 0.66
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