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• On-device Online Learning and Semantic Management of TinyML Systems
  Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning (ML). While many acknowledge the potential benefits of TinyML, its practical implementation presents unique challenges. This study…
  crossref Haoyu Ren, Darko Anicic, Xue Li, Thomas Runkler 2024-05-16T11:25:23Z 置信度 0.82 computer sciencecs.lgcomputer and information sciencescs.aiartificial intelligence
• Perspectives of TinyML-Based Self-management in IoT-Based Systems
  crossref Mohamed Maoui, Rohallah Benaboud 2024-07-30T13:03:51Z 置信度 0.66
• TinyML-based Image Recognition for Real-time Capybara Detection on Resource-constrained Embedded Hardware in Precision Livestock Farming
  The modernization of animal production increasingly relies on precision technologies to improve productivity, reduce operational costs, and maintain animal welfare and environmental sustainability. Among these technologies, artificial intelligence and computer…
  crossref Tung Chiun Wen, Fabiano Gregolin, Késia Oliveira da Silva Miranda, Luana Maria Benicio 等 2026-06-05T06:31:41Z 置信度 0.82 computer scienceartificial intelligencecomputer and information sciencessoftware deploymentdeep learning
• TinyML for Safe Driving: The Use of Embedded Machine Learning for Detecting Driver Distraction
  Among the main causes of road accidents, one of the most significant is related to driver distraction while driving, which is responsible for 18% of car accidents worldwide. This situation has demanded the development of mechanisms to automatically detect this…
  crossref Thommas Flores, Marianne Silva, Mariana Azevedo, Thais Medeiros 等 2023-08-23T13:53:46Z 置信度 0.79 computer sciencecomputer and information sciencesartificial intelligencemachine learningelectrical engineering, electronic engineering, information engineering
• Development of a Cost-Effective Myoelectric Prosthetic Hand with Embedded TinyML for Real-Time Hand Function Rehabilitation
  Amputation of upper or lower limbs often results in significant functional limitations, particularly in hand related tasks. Myoelectric prosthetic hands offer a promising solution to restore functionality and independence for individuals with hand amputations.…
  crossref Md. Alimul Islam, Md. Abu Sayed, Muhammad Muinul Islam, Muhammad Abdul Kadir 2026-06-11T19:58:03Z 置信度 0.82 brain-computer interfacecomputer sciencemotor imageryphysical medicine and rehabilitationinterface
• An Embedded Tinyml Framework for Real-Time Anomaly Detection in Bluetooth Low Energy Wearable Devices
  The behavioral properties have been found to be more efficient in detecting Bluetooth intrusion compared to the traditional protocol-signature-based approaches. To record such behaviors, controlled Bluetooth Low Energy (BLE) attack scenarios such as advertisem…
  crossref Vignesh Kumar M, Sathis Kumar N R, Mariyam Jasmine S A, Pathma Sri S 等 2026-04-01T20:07:19Z 置信度 0.82 cryptography and securitycomputer vision and pattern recognitionconvolutional neural networkcomputer sciencecomputer and information sciences
• TinyML on Arduino Nano 33 BLE for Disabled Person
  crossref Youssef Bouh, Mohamed Baslam, Mohamed Ouhda 2024-02-29T19:02:53Z 置信度 0.66
• A TinyML-Based Edge-AI Module for Microbial Hotspot Detection and Environmental Sensing in Autonomous Field Robotics
  crossref Harini Shrileka, Aadhi Maheshwaran Sri Hari 2026-06-15T07:34:43Z 置信度 0.66
• An Effective Fruit Detection Model Using Convolutional Neural Network with TinyML for Edge Internet of Things Devices for Smart Agriculture
  crossref K Vivek, Ahmad Al-Qerem, Bezawada Sree Nilai, Bharani B R 等 2026-07-07T19:42:05Z 置信度 0.66
• Development of an AI-Based Animal Intrusion Detection System for Agricultural Lands Using ESP32-CAM and TinyML
  Animal invasion is one of the major threats observed recent times in the agricultural lands. This is due to the extension of farm lands to feed the increasing population. There is a need to control this animal invasion without harming the living animals. Hence…
  crossref B. A. Anand, R. Manoj, V. S. Mokshitha, Monika. M. Chowhan 等 2026-06-03T09:52:44Z 置信度 0.82 agriculturecomputer scienceartificial neural networkconvolutional neural networkbiotechnology
• Industry 4.0 Efficiency: Predictive Maintenance with TinyML and an Incremental Model
  crossref Abdelwahed Elmoutaoukkil, Marouane Chriss, Amine Khatib, Ahmed Mouchtachi 2024-12-23T07:02:35Z 置信度 0.66
• Deep Reorganization: Retaining Residuals in TinyML
  Designing intelligent, tiny devices with limited memory is immensely challenging, exacerbated by the additional memory requirement of residual connections in deep neural networks. In contrast to existing approaches that eliminate residuals to reduce peak memor…
  crossref Hashan Roshantha Mendis, Chih-Kai Kang, Chun-Han Lin, Ming-Syan Chen 等 2024-11-07T19:27:22Z 置信度 0.82 computer and information sciencesartificial intelligencecs.lgcomputer vision and pattern recognitioncs.ai
• A Feasibility Study on Tinyml-Based Framework for Categorical Urban Noise Detection Using Low-Cost Sensors: A Systematic Review
  A rise in Urbanisation has vastly increased the number of environmental issues related to urban living, including, most significantly, Noise Pollution, which is now seen as a major Public Health Threat to the residents of contemporary urban centres. Numerous s…
  crossref Batis, Glen Justine P, Castro, Christian Joeffrey B, Moran, Allysa Mae D, Pastor Jr., Jerry R 等 2026-04-01T11:13:41Z 置信度 0.82 medicinemental healthhealth carediseasepsychology
• Edge Impulse vs TensorFlow: A Comparative Analysis of TinyML Platforms for Maize Leaf Disease Identification
  TinyML is an emerging field of machine learning that focuses on developing and deploying models on low-power microcontrollers in embedded systems and Internet of Things devices. Edge Impulse and TensorFlow are two popular machine learning platforms that can be…
  crossref Ewura Abena Essanoah Arthur, Fortunatus Aabangbio Wulnye, Dennis Agyemanh Nana Gookyi, Kwame Opuni-Boachie Obour Agyekum 等 2024-04-25T17:39:17Z 置信度 0.82 computer sciencecomputer and information sciencesmachine learningcloud computingartificial intelligence
• CardioTML: ESP32-Based TinyML Wearable System for Early Identification of Cardiac Risk to Enhance Well-Being and Support Healthcare
  Cardio vascular disease (CVDs) are a leading cause of mortality worldwide, emphasizing the need for early detection and continuous monitoring to reduce emergency outcomes. This paper proposes an ESP32 hybrid edge-cloud wearable platform for real-time cardiac r…
  crossref Nahian Akber Chowdhury, Md Iftakhar Ahsan Jarif, Tanzil Ahmed Rahin, Saruar Hossain Shovo 等 2026-06-11T19:58:03Z 置信度 0.82 computer scienceelectrical engineering, electronic engineering, information engineeringscalabilitywearable computercomputer and information sciences
• TNY-INGEN-eyx: An Optimized TinyML-Based Early Detection of Glaucoma with Severity Level
  crossref Fortunatus Aabangbio Wulnye, Syma Afsha, Kwadwo Owusu Akuffo, Eunice Achampong Boadu 等 2026-07-01T19:34:17Z 置信度 0.66
• Development of a TinyML based four-chamber refrigerator (TBFCR) for efficiently storing pharmaceutical products: Case Study: Pharmacies in Rwanda
  Medical products are very sensitive to temperature; the improper temperature may lead to their inefficacity. Apart from products that are stored at room temperature, remaining medical products are stored in electronically controlled refrigerators. A lot of res…
  crossref Joseph HABIYAREMYE, Marco ZENNARO, Chomora MIKEKA, Emmanuel MASABO 2022-06-21T20:27:55Z 置信度 0.79 computer sciencecloud computingbig datacs.lginternet of things
• TinyForge: A Design Space Exploration to Advance Energy and Silicon Area Trade-offs in tinyML Compute Architectures with Custom Latch Arrays
  The proliferation of smart IoT devices has given rise to tinyML, which deploys deep neural networks on resource-constrained systems, benefitting from custom hardware that optimizes for low silicon area and high energy efficiency amidst tinyML's characteristic …
  crossref Massimo Giordano, Rohan Doshi, Qianyun Lu, Boris Murmann 2024-04-24T12:08:21Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringcs.lgcomputer sciencecryptography and security
• A.I. NEURAL NETWORKS INFERENCE INTO THE IOT EMBEDDED DEVICES USING TINYML FOR PATTERN DETECTION WITHIN A SECURITY SYSTEM
  The paper presents the implementation challenges of a proof of concept development for image processing with artificial intelligence neural network into an embedded device.Because of the hardware constraints of the embedded devicee.g.16 KB RAM and 128 KB of EE…
  crossref Cristian TOMA, Marius POPA, Mihai DOINEA 2020-12-15T03:13:12Z 置信度 0.79 computer scienceartificial neural networkcs.neconvolutional neural networkartificial intelligence
• muRISCV-NN: Challenging Zve32x Autovectorization with TinyML Inference Library for RISC-V Vector Extension
  With the rapid adoption of deep learning workloads to resource-constrained edge devices, efficient and data-parallel computing paradigms are becoming increasingly important. The RISC-V ISA provides a set of vector extensions featuring powerful data computation…
  crossref Philipp van Kempen, Jefferson Parker Jones, Daniel Mueller-Gritschneder, Ulf Schlichtmann 2024-07-01T06:19:21Z 置信度 0.82 computer and information sciencescryptography and securitymachine learningcomputer scienceconvolutional neural network
• TinyML-Enabled Intelligent Question-Answer Services in IoT Edge Consumer Devices
  Presently, the prevalence of large language models has driven the rapid popularization of question-and-answer applications. However, the training and deployment of large language models involve high-resource computing, posing a challenge for many small or edge…
  crossref Xuan Wu, Xuanye Lin, Zhen Zhang, Chien-Ming Chen 等 2024-07-08T17:29:39Z 置信度 0.82 computer and information sciencescomputer sciencecs.lgcs.aics.cl
• A TinyML-based Predictive Maintenance Framework for DC Motor Systems with Lightweight WebXR Digital Twin Visualization
  crossref I Nyoman Kusuma Wardana, I Wayan Budi Sentana, Putri Alit Widyastuti Santiary, Ida Bagus Irawan Purnama 等 2026-08-14T19:34:41Z 置信度 0.66
• TinyML for Eddy Current Testing: A Review of Advances, Challenges, and Applications
  Eddy current testing (ECT) is a widely adopted electromagnetic non-destructive testing (NDT) technique for detecting defects in conductive materials. In practical deployments, however, ECT systems often suffer from low signal-to-noise ratio, strong sensitivity…
  crossref Shanming Qin, Yingchun Chen, Md Masuduzzaman, Chengshun Xu 等 2026-04-20T20:05:57Z 置信度 0.82 computer and information sciencescomputer scienceartificial intelligenceelectrical engineering, electronic engineering, information engineeringmaterials science
• A Primer for tinyML Predictive Maintenance: Input and Model Optimisation
  crossref Emil Njor, Jan Madsen, Xenofon Fafoutis 2022-06-16T11:52:13Z 置信度 0.64
• ACTION: Automated Hardware-Software Codesign Framework for Low-precision Numerical Format SelecTION in TinyML
  crossref Hamed F. Langroudi, Vedant Karia, Tej Pandit, Becky Mashaido 等 2022-07-13T13:06:54Z 置信度 0.64
• Security, Privacy, and Trust Challenges in TinyML-Based IoT Edge Intelligence
  The rapid expansion of the Internet of Things (IoT) has driven a shift toward distributed intelligence, where interconnected devices generate and process large-scale data in real time. Traditional cloud-centric architectures face limitations including high lat…
  crossref Obinna Emmanuel Obi-Akwari, Meletius Mgbeodichimma Igbokwe, Itohowo Effiong Charles, Muinat Taiwo Adedokun 等 2026-07-30T13:14:57Z 置信度 0.82 computer sciencecloud computingcomputer and information sciencescomputer securityinternet of things
• Demo: Battery-free TinyML Made Easy with Riotee
  This demo uses a machine-learning-based hot word detection application to showcase the capabilities of Riotee, an open-source and commercially available hardware-software platform for the battery-free Internet of Things. We describe the Riotee hardware consist…
  crossref Kai Geissdoerfer, Marco Zimmerling 2024-11-04T18:48:26Z 置信度 0.82 cs.sdconvolutional neural networkinternet of thingsquantum physicseess.as
• TinyML-Based Swine Vocalization Pattern Recognition for Enhancing Animal Welfare in Embedded Systems
  The automatic recognition of animal vocalizations is a valuable tool for monitoring pigs’ behavior, health, and welfare. This study investigates the feasibility of implementing a convolutional neural network (CNN) model for classifying pig vocalizations using …
  crossref Tung Chiun Wen, Caroline Ferreira Freire, Luana Maria Benicio, Giselle Borges de Moura 等 2025-07-04T03:47:26Z 置信度 0.82 artificial intelligencecomputer sciencebrain-computer interfaceconvolutional neural networkcomputer and information sciences
• TinyML-Enabled Frugal Smart Objects: Challenges and Opportunities
  The TinyML paradigm proposes to integrate Machine Learning (ML)-based mechanisms within small objects powered by Microcontroller Units (MCUs). This paves the way for the development of novel applications and services that do not need the omnipresent processing…
  crossref Ramon Sanchez-Iborra, Antonio F. Skarmeta 2020-08-13T16:30:43Z 置信度 0.79 computer sciencecloud computinginternet of thingskeyedge computing
• Architectural Design for Inspection of Machine Objects Using Small DNNs as TinyML for Machine Vision of Defects and Faults in the Manufacturing Processes
  crossref Raj Kamal, Aabha Jain, Manojkumar Vilasrao Deshpande 2021-09-03T21:31:02Z 置信度 0.64
• A Modular IoT-TinyML Architecture for Early Detection of Citrus Diseases
  crossref José David Ortiz Cuadros, Oscar Agustín Loyola Valenzuela, Santiago Murillo Rendón 2026-08-06T15:52:17Z 置信度 0.66
• Adaptive smart aquaculture environmental control using deep reinforcement learning on TinyML platforms
  crossref Yi-Chih Tung, Faril Pirwanhadi, I Gusti Nyoman Anton Surya Diputra, En-Cheng Liou 2026-06-14T17:28:31Z 置信度 0.66
• Vision-Based Supervisory Concept for Manual Assembly Operations Using TinyML on an STM32 Microcontroller
  crossref Jakub Kaščak, Lucia Knapčíková, Martin Pollák, Zuzana Mitaľová 等 2026-05-03T23:25:30Z 置信度 0.66
• Probabilistic Caching Strategy and TinyML-Based Trajectory Planning in UAV-Assisted Cellular IoT System
  Unmanned aerial vehicles (UAVs) deployed as an aerial assisted base station has the characteristics of flexibility and mobility. As an effective way to reduce the communication pressure of network center, content edge caching combined with UAV-assisted network…
  crossref Xin Gao, Xue Wang, Zhihong Qian 2024-01-31T18:38:03Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringnetworking and internet architecturecs.nisignal processing
• Developing a TinyML-Oriented Deep Learning Model for an Intelligent Greenhouse Microclimate Control from Multivariate Sensed Data
  crossref Ilham Ihoume, Rachid Tadili, Nora Arbaoui, Mohamed Benchrifa 等 2023-01-24T18:02:39Z 置信度 0.64
• A data-stream TinyML compression algorithm for vehicular applications: a case study
  The Internet of Things (IoT) has reduced the distance between one point and another and between people by connecting multiple devices to the web. However, the volume and speed of data creation and transmission have also increased. In this scenario, some challe…
  crossref Marianne Silva, Gabriel Signoretti, Thommas Flores, Pedro Andrade 等 2022-07-22T16:42:05Z 置信度 0.79 computer scienceinternet of thingscloud computingcryptography and securitybig data
• Metodologia baseada em TinyML para Estimar as Emissões de CO2: Uma Análise Comparativa entre Abastecimentos de veículo com Etanol e Gasolina
  One of the main environmental concerns lies in greenhouse gas emissions originating from vehicles, particularly carbon dioxide (CO2). The transition to cleaner vehicles and the consequent reduction of these emissions are emerging as global trends. In this cont…
  crossref Tatiane Gois, Matheus Andrade, Thaís Medeiros, Marianne Silva 等 2025-01-24T17:31:41Z 置信度 0.82 greenhouse gasenvironmental scienceenvironmental economicscarbon fibersnatural resource economics
• A Heterogeneous TinyML SoC with Energy-Event-Performance-Aware Management and Compute-in-Memory Two-Stage Event-Driven Wakeup
  This paper presents a heterogeneous TinyML SoC with E2p- aware system-level energy management achieving a minimum 3.5µW and 30000× peak-to-idle power ratio. The energy-event-performance (E2P)-aware management utilizes various fully synthesizable monitors to be…
  crossref Yanchi Dong, Xueping Liu, Kangbo Bai, Guoxiang Li 等 2024-08-26T17:23:31Z 置信度 0.82 renewable energycryptography and securitycs.nienergydistributed computing
• Fault Injection Attacks and Countermeasures on TinyML Algorithms
  crossref Anthony Etim, Srilalith Nampally, Aubtin Rasouli, Dustin Mazza 等 2026-07-15T20:02:14Z 置信度 0.66
• TEDA-RLS: A TinyML Incremental Learning Approach for Outlier Detection and Correction
  The Internet of Things (IoT) paradigm encompasses computing and networking capabilities within electronic objects, acting as a fundamental development framework with vast potential for improving lives, enhancing industrial processes, and enabling real-time dec…
  crossref Pedro Andrade, Marianne Silva, Morsinaldo Medeiros, Daniel G. Costa 等 2024-09-17T18:55:04Z 置信度 0.82 computer scienceartificial intelligencecloud computingmachine learningdata science
• SAMS: a sustainable agri-tech monitoring system integrating IoT and TinyML for smart farming
  crossref Md Abdul Hamid, S. M. Nuruzzaman Nobel, Md Ashraful Hossain, Md Asif Imran 等 2026-04-06T07:59:20Z 置信度 0.66
• Augmenting Embodied Learning in Welding Training: The Co-Design of an XR- and tinyML-Enabled Welding System for Creative Arts and Manufacturing Training
  Metal welding is a craft manufacturing skill that can be unusually difficult to externalize and represent to novices. Building competency requires an apprentice to iteratively practice embodied skills and sensitize themselves to a sensorially complex practice.…
  crossref Zhenfang Chen, Tate Johnson, Andrew Knowles, Ann Li 等 2024-01-24T18:17:01Z 置信度 0.82 psychologyembodied cognitionhuman-computer interactionsociologycomputer and information sciences
• Designing resilient IoT and Edge Computing with federated tinyML
  crossref Paul Laiu, Mingyan Li, Jeffrey A. Nichols, Mike Huettel 等 2026-02-06T16:32:03Z 置信度 0.66
• A Deep Learning-Powered TinyML Model for Gesture-Based Air Handwriting Simple Arabic Letters Recognition
  crossref Ismail Lamaakal, Yassine Maleh, Ibrahim Ouahbi, Khalid El Makkaoui 等 2024-08-31T03:44:48Z 置信度 0.66
• Lightweight Convolutional Neural Networks Framework for Really Small TinyML Devices
  crossref César A. Estrebou, Martín Fleming, Marcos D. Saavedra, Federico Adra 等 2022-03-29T20:06:14Z 置信度 0.64
• On the adversarial robustness of full integer quantized TinyML models at the edge
  The recent surge in deploying machine learning (ML) models at the edge has revolutionized various industries by enabling real-time decision-making on resource-constrained devices, such as TinyML models on microcontrollers. However, this trend brings forth a cr…
  crossref Davy Preuveneers, Willem Verheyen, Sander Joos, Wouter Joosen 2023-11-16T18:16:31Z 置信度 0.79 edge computingcloud computingcryptography and securitydrug discoverycs.lg
• Enhancing the Energy Efficiency and Robustness of tinyML Computer Vision Using Coarsely-quantized Log-gradient Input Images
  This article studies the merits of applying log-gradient input images to convolutional neural networks (CNNs) for tinyML computer vision (CV). We show that log gradients enable: (i) aggressive 1-bit quantization of first-layer inputs, (ii) potential CNN resour…
  crossref Qianyun Lu, Boris Murmann 2023-04-08T10:32:16Z 置信度 0.79 artificial intelligencecomputer and information sciencesconvolutional neural networkcomputer sciencemachine learning
• TrustEdge: A quantum-safe, self-healing framework for federated TinyML in critical ICU monitoring
  crossref Umar Hayat Khan, Affaq Qamar, Rahim Khan, Mohamad A. Alawad 等 2025-12-18T00:38:24Z 置信度 0.66
• Medusa: A 0.83/4.6 ${\mu}\mathrm{J}$/Frame 86/91.6%-CIFAR-10 TinyML Processor with Pipelined Pixel Streaming of Bottleneck Layers in 28nm CMOS
  Medusa is a 28 nm programmable 8-bit processor that achieves state-of-the-art inference energy across a range of always-on tiny Machine Learning (tinyML) tasks. It features custom 6T-latch-based Inner Loop Memories (ILMs) optimized for tinyML that achieve a re…
  crossref Rohan Doshi, Massimo Giordano, Justin Olah, Zhidong Cao 等 2024-08-26T17:23:31Z 置信度 0.82 computer and information sciencescs.lgcryptography and securityelectrical engineering, electronic engineering, information engineeringneuromorphic engineering
• TinyML on Mobile Devices for Hybrid Energy Management Systems
  This paper explores the potential of Tiny Machine Learning (TinyML) for privacy-preserving building energy management systems on mobile devices. While TinyML offers reduced latency and improved privacy, its effectiveness in predicting building energy consumpti…
  crossref Olha Boiko, Anton Komin, Vira Shendryk, Reza Malekian 等 2024-10-31T17:32:03Z 置信度 0.82 computer sciencecomputer and information sciencescloud computingedge computingelectrical engineering, electronic engineering, information engineering
• LOPdM: A Low-Power On-Device Predictive Maintenance System Based on Self-Powered Sensing and TinyML
  Predictive maintenance (PdM) has emerged as a prominent strategy that can recognize the current state and predict the future trend of machines. It helps prevent disastrous breakdowns. Such systems were mostly realized based on artificial intelligence (AI) mode…
  crossref Zijie Chen, Yiming Gao, Junrui Liang 2023-08-24T17:22:29Z 置信度 0.79 computer scienceartificial intelligencecomputer and information sciencescs.lgelectrical engineering, electronic engineering, information engineering
• TinyEP: TinyML-Enhanced Energy Profiling for Extreme Edge Devices
  The widespread integration of the Internet of Things (IoT) into daily operations has made optimizing energy consumption in low-power edge devices increasingly important. This is especially critical when battery-powered IoT platforms, like sensor networks, are …
  crossref Kilian Müller, Johannes Weidner, Norman Franchi, Peter Wägemann 2024-12-18T19:40:41Z 置信度 0.82 computer sciencecomputer and information scienceselectrical engineering, electronic engineering, information engineeringcs.aisignal processing
• ML-HW Co-Design of Noise-Robust TinyML Models and Always-On Analog Compute-in-Memory Edge Accelerator
  Always-on TinyML perception tasks in Internet of Things applications require very high energy efficiency. Analog compute-in-memory (CiM) using nonvolatile memory (NVM) promises high energy efficiency and self-contained on-chip model storage. However, analog Ci…
  crossref Chuteng Zhou, Fernando García Redondo, Julian Büchel, Irem Boybat 等 2022-08-19T19:37:40Z 置信度 0.79 computer and information sciencescomputer sciencecs.lgartificial intelligencephysical sciences
• On The Dynamic Ensemble Selection for TinyML-based Systems - a Preliminary Study
  crossref Tobiasz Puślecki, Krzysztof Walkowiak 2026-05-07T11:35:59Z 置信度 0.66
• TinyML Empowered Transfer Learning on the Edge
  Tiny machine learning (TinyML) is a promising approach to enable intelligent applications relying on Human Activity Recognition (HAR) on resource-limited and low-power internet of things (IoT) edge devices. However, designing efficient TinyML models for these …
  crossref Ali M. Hayajneh, Maryam Hafeez, Syed Ali Raza Zaidi, Des McLernon 2024-03-05T14:24:27Z 置信度 0.82 computer and information sciencescomputer sciencecs.lgcs.aiartificial intelligence
• Real-Time Arabic Digit Spotting with TinyML-Optimized CNNs on Edge Devices
  crossref Yasmine Abu Adla, Mazen A. R. Saghir, Mariette Awad 2023-05-31T08:03:56Z 置信度 0.64
• An unsupervised TinyML approach applied to the detection of urban noise anomalies under the smart cities environment
  Artificial Intelligence of Things (AIoT) is an emerging area of interest, and this can be used to obtain knowledge and take better decisions in the same Internet of Things (IoT) devices. IoT data are prone to anomalies due to various factors such as malfunctio…
  crossref Sahibzada Saadoon Hammad, Ditsuhi Iskandaryan, Sergio Trilles 2023-06-30T12:08:04Z 置信度 0.79 computer scienceinternet of thingscomputer and information scienceselectrical engineering, electronic engineering, information engineeringconvolutional neural network
• An Ultra-Low Power TinyML System for Real-Time Visual Processing at Edge
  Tiny machine learning (TinyML), executing AI workloads on resource and power strictly restricted systems, is an important and challenging topic. This brief firstly presents an extremely tiny backbone to construct high efficiency CNN models for various visual t…
  crossref Kunran Xu, Huawei Zhang, Yishi Li, Yuhao Zhang 等 2023-01-23T19:36:23Z 置信度 0.79 computer and information sciencesartificial intelligencecomputer scienceelectrical engineering, electronic engineering, information engineeringdistributed computing
• TinyML-based Wearable ECG Monitoring Systems: State-of-the-Art Survey on Architectures, Optimizations and Deployment Challenges towards Future Directions
  crossref Saja B. Attallah, Rand A. Atta, Zaid Ameen Abduljabbar, Ruslan S. Naseef 等 2026-07-24T19:12:37Z 置信度 0.66
• RAMAN: A Reconfigurable and Sparse tinyML Accelerator for Inference on Edge
  Deep Neural Network (DNN) based inference at the edge is challenging as these compute, and data-intensive algorithms need to be implemented at low cost and low power while meeting the latency constraints of the target applications. Sparsity, in both activation…
  crossref Adithya Krishna, Srikanth Rohit Nudurupati, D. G. Chandana, Pritesh Dwivedi 等 2024-04-12T13:31:25Z 置信度 0.82 computer and information sciencescs.lgcomputer sciencecryptography and securityelectrical engineering, electronic engineering, information engineering
• Ocular Disease Recognition Using TinyML for Efficient Android Implementation
  crossref Cococi Alin-Gabriel, Dogaru Radu 2024-08-29T17:03:35Z 置信度 0.66
• Hybrid Convolution and Vision Transformer NAS Search Space for TinyML Image Classification
  crossref Mikhael Djajapermana, Moritz Reiber, Daniel Mueller-Gritschneder, Ulf Schlichtmann 2026-05-07T11:34:33Z 置信度 0.66
• TinyML Model for Fault Classification of Photovoltaic Modules Based on Visible Images
  crossref Z. Ksira, N. Blasuttigh, A. Mellit, A. Massi Pavan 2024-05-27T05:01:47Z 置信度 0.66
• FlashMAC: A Time-Frequency Hybrid MAC Architecture With Variable Latency-Aware Scheduling for TinyML Systems
  With the widespread of deep neural networks (DNNs) in diverse applications, tiny platforms such as Internet-of-Things devices are starting to adopt DNNs. Due to their extreme energy and form factor constraints, conventional digital-only implementations of mult…
  crossref Surin Gweon, Sanghoon Kang, Kwantae Kim, Hoi-Jun Yoo 2022-07-04T20:01:12Z 置信度 0.79 computer and information sciencescomputer scienceelectrical engineering, electronic engineering, information engineeringenergy storagesignal processing
• TinyML Design Contest for Life-Threatening Ventricular Arrhythmia Detection
  The first ACM/IEEE TinyML Design Contest (TDC) held at the 41st International Conference on Computer-Aided Design (ICCAD) in 2022 is a challenging, multimonth, research and development competition. TDC’22 focuses on real-world medical problems that require the…
  crossref Zhenge Jia, Dawei Li, Cong Liu, Liqi Liao 等 2023-08-29T17:51:31Z 置信度 0.79 computer sciencecs.aics.cveess.ivdrug discovery
• Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML)
  Machine Learning (ML) on the edge is key for enabling a new breed of IoT and autonomous system applications. The departure from the traditional cloud-centric architecture means that new deployments can be more power-efficient, provide better privacy and reduce…
  crossref Syed Ali Raza Zaidi, Ali M. Hayajneh, Maryam Hafeez, Q. Z. Ahmed 2022-09-16T15:35:52Z 置信度 0.79 computer sciencecloud computingedge computingscalabilityinternet of things
• TinyML-Based Intrusion Detection System for In-Vehicle Network Using Convolutional Neural Network on Embedded Devices
  This letter proposes a novel model for effectively detecting malicious messages in controller area network (CAN) communication, which is widely used in automotive networks. Because in-vehicle networks operate in resource-constrained environments, an intrusion …
  crossref Hyungchul Im, Seongsoo Lee 2024-10-07T13:45:09Z 置信度 0.82 computer and information sciencescomputer scienceelectrical engineering, electronic engineering, information engineeringconvolutional neural networkartificial intelligence
• Evaluation of the Energy Viability of Smart IoT Sensors Using TinyML for Computer Vision Applications: A Case Study
  crossref Adriel Monti De Nardi, Maxwell Eduardo Monteiro 2023-10-25T23:03:00Z 置信度 0.64
• Toward Stroke Rehabilitation: A Single Hand-Mounted IMU and TinyML for Therapeutic Hand Movement Recognition
  Abstract Stroke rehabilitation requires repetitive, consistent physical therapy to restore upper-limb motor function. However, traditional supervised therapy is resource-intensive and often inaccessible for home-based recovery. This paper presents a low-cost, …
  crossref Xiru Chen, Sohail Ahmed Soomro, Georgi V. Georgiev 2026-06-15T11:35:20Z 置信度 0.82 computer scienceartificial intelligencebrain-computer interfacecomputer and information sciencesmachine learning
• Short Paper: Counting Parked Bicycles on the Edge - A TinyML Smart City Application
  crossref Jan Stenkamp, Mathis Hunke, Cem Karatas, Steffen Kirchhoff 等 2026-05-08T14:20:14Z 置信度 0.66
• Online Processing of Vehicular Data on the Edge Through an Unsupervised TinyML Regression Technique
  The Internet of Things (IoT) has made it possible to include everyday objects in a connected network, allowing them to intelligently process data and respond to their environment. Thus, it is expected that those objects will gain an intelligent understanding o…
  crossref Pedro Andrade, Ivanovitch Silva, Marianne Diniz, Thommas Flores 等 2023-04-08T06:32:16Z 置信度 0.79 computer scienceartificial intelligencecomputer and information sciencescs.lgdata science
• Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions
  The advancement of sustainable energy sources necessitates the development of robust forecasting tools for efficient energy management. A prominent player in this domain, solar power, heavily relies on accurate energy yield predictions to optimize production, …
  crossref Ali M. Hayajneh, Feras Alasali, Abdelaziz Salama, William Holderbaum 2024-01-15T21:21:58Z 置信度 0.82 computer sciencerenewable energyengineeringenvironmental economicsbusiness
• Developing a TinyML Image Classifier in an Hour
  Tiny machine learning technologies are bringing intelligence ever closer to the sensor, thus enabling the key benefits of edge computing (e.g., reduced latency, improved data security, higher energy efficiency, and lower bandwidth consumption, also without the…
  crossref Riccardo Berta, Ali Dabbous, Luca Lazzaroni, Danilo Pietro Pau 等 2024-08-29T14:13:59Z 置信度 0.82 computer scienceartificial intelligencecs.lgcomputer and information sciencescloud computing
• TinyML-Enabled IoT Edge Framework With Knowledge Distillation for Weed Classification
  Weed classification is a fundamental perception task for agricultural robots and an essential enabler of precision and sustainable farming. Existing solutions often rely on high-power edge computing platforms, which limit long-term autonomous operation in Inte…
  crossref Yuxuan Zhang, Yuchen Lu, Luciano Sebastian Martinez-Rau, Zhengqiang Fan 等 2026-03-31T19:53:28Z 置信度 0.82 computer and information sciencescomputer sciencecs.lgelectrical engineering, electronic engineering, information engineeringartificial intelligence
• STEP-SAFE: Smart TinyML and Few-Shot Enabled Pedestrian and Cyclist Safety Framework for Intelligent Transportation System
  Ensuring the safety of pedestrians and cyclists referred to as vulnerable road users (VRUs) is a central challenge in intelligent transportation systems (ITS), especially in urban environments where visual occlusion, behavioral unpredictability, and data scarc…
  crossref Maira Khalid, Ahmed Raza Mohsin, Jehad Ali, Houbing Herbert Song 等 2026-04-01T20:14:11Z 置信度 0.82 computer sciencecomputer and information sciencesartificial intelligencecs.lgcs.ai
• Atrial Fibrillation and Sinus Rhythm Detection Using TinyML (Embedded Machine Learning)
  crossref Guilherme V. B. F. Silva, Mateus D. Lima, José A. F. Filho, Marcelo J. Rovai 2024-01-03T05:02:57Z 置信度 0.66
• A TinyML Platform for On-Device Continual Learning With Quantized Latent Replays
  In the last few years, research and development on Deep Learning models & techniques for ultra-low-power devices– in a word,TinyML– has mainly focused on atrain-then-deployassumption, with static models that cannot be adapted to newly collected data without cl…
  crossref Leonardo Ravaglia, Manuele Rusci, Davide Nadalini, Alessandro Capotondi 等 2021-10-20T18:02:18Z 置信度 0.79 computer and information sciencescomputer sciencecs.lgartificial intelligenceelectrical engineering, electronic engineering, information engineering
• Autonomous Solar-Powered Smart Sensing Node: Integrating TinyML and Hybrid LoRaWAN/Wi-Fi Connectivity for Sustainable Precision Agriculture
  Precision agriculture and sustainable farming practices require autonomous environmental monitoring systems capable of operating in remote areas with limited energy and connectivity. However, the high cost of existing professional technology remains a signific…
  crossref Elizabeth Ospina-Rojas, Juan Sebastián Botero-Valencia, Juan Guillermo Muñoz-Cataño, Juan Carlos Morales-Guerra 等 2026-08-03T12:57:56Z 置信度 0.82 computer sciencecomputer and information scienceselectrical engineering, electronic engineering, information engineeringengineeringunderwater
• Agentic TinyML for Intent-Aware Handover in 6G Wireless Networks
  As sixth-generation (6G) wireless networks evolve into increasingly Artificial Intelligence (AI)-driven, user-centric ecosystems, traditional reactive handover mechanisms demonstrate limitations, especially in mobile edge computing and autonomous agent-based s…
  crossref Alaa Saleh, Roberto Morabito, Sasu Tarkoma, Anders Lindgren 等 2025-12-29T18:40:07Z 置信度 0.82 computer and information sciencescomputer scienceelectrical engineering, electronic engineering, information engineeringnetworking and internet architecturesignal processing
• Efficient On-Device Domain Learning for Keyword Spotting on Ultra-Low-Power Platforms
  Deep Neural Network-based Keyword Spotting accuracy degrades in noisy environments. On-site adaptation to previously unseen noise is crucial to recover accuracy loss, and on-device learning is required in scenarios where adaptation has to happen in the field. …
  crossref Cristian Cioflan, Lukas Cavigelli, Manuele Rusci, Miguel de Prado 等 2026-01-14T20:41:02Z 置信度 0.82 cs.lgcomputer and information sciencescs.aics.cvcs.cl
• Federated learning for IoT devices: Enhancing TinyML with on-board training
  The spread of the Internet of Things (IoT) involving an uncountable number of applications, combined with the rise of Machine Learning (ML), has enabled the rapid growth of pervasive and intelligent systems in a variety of domains, including healthcare, enviro…
  crossref M. Ficco, A. Guerriero, E. Milite, F. Palmieri 等 2023-12-08T02:51:53Z 置信度 0.79 computer sciencecomputer and information sciencescs.lgmachine learningartificial intelligence
• A Folded-Differential Switched-Capacitor SRAM CIM Macro With Scalable MAC Sizes for TinyML Inference
  crossref Zhonghao Chen, Ling-An Cheong, Tianyi Yu, Yiming Chen 等 2026-02-06T20:52:44Z 置信度 0.66
• Lyapunov-Guided Long-Term Fairness-Aware Federated Learning for Collaborative TinyML on Edge Devices
  Although federated learning (FL) has become a privacy-preserving machine learning paradigm that enables a collaborative form of tiny machine learning (TinyML) on edge devices, unfairness may arise when the performance of the global model varies due to heteroge…
  crossref Jianfeng Lu, Yuhang Sheng, Shuqin Cao, Said Elnaffar 等 2024-05-07T13:28:30Z 置信度 0.82 computer and information sciencescs.lgcryptography and securityelectrical engineering, electronic engineering, information engineeringsignal processing
• Hardware/Software Co-Design for TinyML Voice-Recognition Application on Resource Frugal Edge Devices
  On-device artificial intelligence has attracted attention globally, and attempts to combine the internet of things and TinyML (machine learning) applications are increasing. Although most edge devices have limited resources, time and energy costs are important…
  crossref Jisu Kwon, Daejin Park 2021-11-23T02:55:17Z 置信度 0.79 cloud computingedge computinginternet of thingsbig datawearable computer
• Enhancing TinyML-Based Container Escape Detectors With Systemcall Semantic Association in UAVs Networks
  The adoption of lightweight container technology enables the cross-architecture deployment of Tiny Machine Learning (TinyML) models, while the implementation of container escape detectors ensures the security of both models and applications. However, a signifi…
  crossref Tao Zheng, Yunxiang Qiu, Yundan Zheng, Qixu Wang 等 2024-02-22T19:52:56Z 置信度 0.82 computer sciencecomputer and information sciencescs.aics.lgartificial intelligence
• Design and Implementation of UFRJ Nautilus’ AUV Lua - A TinyML Approach
  The UFRJ Nautilus is a student-driven engineering project team at the Federal University of Rio de Janeiro focused on building and designing AUVs to compete in the AUSVI RoboSub Competition. The team focused on the making of a brand new autonomous underwater v…
  crossref Lara F. de Amorim, Vitor A. Pavani, Lucas B. Alexandre, Pedro H. Teixeira 等 2022-12-13T19:43:02Z 置信度 0.79 underwatercs.roalgorithmsignal processingrobot
• TinyAirNet: TinyML Model Transmission for Energy-Efficient Image Retrieval From IoT Devices
  This letter introduces an energy-efficient pull-based data collection framework for Internet of Things (IoT) devices that use Tiny Machine Learning (TinyML) to interpret data queries. A TinyML model is transmitted from the edge server to the IoT devices. The d…
  crossref Junya Shiraishi, Mathias Thorsager, Shashi Raj Pandey, Petar Popovski 2024-08-01T18:00:51Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringsignal processingnetworking and internet architecturecloud computing
• Integration of TinyML-based proximity and couch sensing in wearable devices for monitoring infectious disease's social distance compliance
  With the advent of artificial intelligence (AI) and Internet of Things (IoT), there has been a rapid increase in the use of sensors to intelligently monitor the environment and movement of objects. Smart solutions have been widely used for monitoring infectiou…
  crossref Ritha M. Umutoni, Marvin M. Ogore, Rosette L. Savanna, Damien Hanyurwimfura 等 2023-06-20T15:40:57Z 置信度 0.79 computer scienceartificial intelligencehuman-computer interactionmedicineconvolutional neural network
• A Heterogeneous TinyML SoC With Systematic Minimum Energy Searching and Management for Keyword Spotting
  Edge AI deploys diversified AI models on resource-constrained tiny machine learning (TinyML) SoCs, which often have heterogeneous architectures, including both microprocessors and domain-specific accelerators. Considering the tight power and performance constr…
  crossref Yanchi Dong, Xueping Liu, Kangbo Bai, Guoxiang Li 等 2025-11-04T18:36:17Z 置信度 0.82 computer and information sciencesrenewable energyenergy storagedistributed computingprocess engineering
• Machine Learning Assisted Tilt/Azimuth Estimation for Passive Stylus Pens Using Position-Based Model Selection
  This paper presents an advanced machine learning (ML) model to calculate the tilt and azimuth angles of passive stylus pens on display panels. Based on real-world sensing data, preprocessing steps were developed to automatically capture data windows centered o…
  crossref Junseo Jo, Hyungcheol Shin, Youngjoo Lee 2026-05-05T20:03:46Z 置信度 0.82 cs.lgcomputer and information sciencesconvolutional neural networkartificial neural networkcomputer science
• Intelligent WSN Sensor Node for Oil Spill Detection and Classification: A TinyML and IoT Approach
  This paper proposes a novel intelligent sensor node architecture that integrates embedded systems, machine learning, and Internet of Things (IoT) technologies. The proposed sensor node plays a crucial role in a Wireless Sensor Network (WSN) designed to detect …
  crossref Yan Ferreira Da Silva, Raimundo Carlos Silvério Freire, João Viana Da Fonseca Neto 2026-06-12T19:43:27Z 置信度 0.82 computer scienceartificial intelligencecomputer and information scienceselectrical engineering, electronic engineering, information engineeringartificial neural network
• 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…
  crossref Kieran Woodward, Eiman Kanjo, Georgios Papandroulidakis, Shady Agwa 等 2026-01-19T20:56:18Z 置信度 0.82 computer sciencecomputer and information sciencesedge computingcs.lgelectrical engineering, electronic engineering, information engineering
• An 1.34-TOPS/W, 16-GOPS/mm ² Digital In-Memory-Computing-Based AI Microcontroller Unit Supporting On-Chip Model Fine-Tuning for a TinyML Device
  crossref Chuan-Tung Lin, Seunghyun Moon, Paul Xuanyuanliang Huang, Mingoo Seok 2026-08-31T19:08:21Z 置信度 0.66
• On-Chip Processing-Assisted TinyML for Heart Rate Monitoring Using Noncontact Ballistocardiogram-Based Bed Sensor
  Heart rate monitoring (HRM) serves as a fundamental component of healthcare systems, providing essential insights into cardiovascular function and overall physiological status. Conventional HRM systems commonly rely on contact-based sensing modalities, such el…
  crossref Quy Phuong Le, Truong Tien Vo, Dogeon Ha, Le The Anh Vi 等 2026-02-20T21:18:28Z 置信度 0.82 computer and information sciencescomputer scienceelectrical engineering, electronic engineering, information engineeringartificial intelligencecs.lg
• Advancements in TinyML: Applications, Limitations, and Impact on IoT Devices
  Artificial Intelligence (AI) and Machine Learning (ML) have experienced rapid growth in both industry and academia. However, the current ML and AI models demand significant computing and processing power to achieve desired accuracy and results, often restricti…
  crossref Abdussalam Elhanashi, Pierpaolo Dini, Sergio Saponara, Qinghe Zheng 2024-09-09T04:15:01Z 置信度 0.82 computer sciencecloud computingdata sciencecomputer securityinternet of things
• Integration of TinyML and LargeML: A Survey of 6G and Beyond
  The evolution from fifth-generation (5G) to sixth-generation (6G) networks is driving an unprecedented demand for advanced machine learning (ML) solutions. Deep learning has already demonstrated significant impact across mobile networking and communication sys…
  crossref Thai-Hoc Vu, Ngo Hoang Tu, Thien Huynh-The, Miroslav Voznak 等 2026-03-16T20:16:26Z 置信度 0.82 computer sciencecomputer and information sciencesartificial intelligencecloud computingelectrical engineering, electronic engineering, information engineering
• An Energy Harvesting Algorithm for UAV-Assisted TinyML Consumer Electronic in Low-Power IoT Networks
  Unmanned aerial vehicles (UAVs) play a crucial role in remote environmental applications of the Low-Power Internet of Things (IoT) network. However, the interaction between a large number of consumer electronics products integrated with TinyML (CEP-TML) may le…
  crossref Jie Huang, Tao Yu, Chinmay Chakraborty, Fan Yang 等 2024-06-27T20:13:52Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringnetworking and internet architecturesignal processinginformation theory
• A Gas Leakage Detection Device Based on the Technology of TinyML †
  Internet of Things devices are frequently used as consumer devices to provide digital solutions, such as smart lighting and digital voice-activated assistants, but they are also employed to alert residents in the instance of an emergency. Given the increasingl…
  crossref Vasileios Tsoukas, Anargyros Gkogkidis, Eleni Boumpa, Stefanos Papafotikas 等 2023-03-22T06:35:28Z 置信度 0.79 computer scienceinternet of thingscloud computingdistributed computingcomputer security
• Robustness in TinyML: A Systematic Literature Review
  TinyML enables the deployment of machine learning models on low-power embedded devices, offering energy-efficient solutions for real-world applications. However, TinyML faces significant challenges due to strict memory, processing, and energy constraints, maki…
  crossref Emanuel Pereira, Erick Barboza, Ícaro Araújo, Saulo dos Santos 等 2026-06-11T20:59:09Z 置信度 0.82 computer sciencecomputer and information sciencesartificial intelligencemachine learningcs.lg
• A Review on the emerging technology of TinyML
  Tiny Machine Learning (TinyML) is an emerging technology proposed by the scientific community for developing autonomous and secure devices that can gather, process, and provide results without transferring data to external entities. The technology aims to demo…
  crossref Vasileios Tsoukas, Anargyros Gkogkidis, Eleni Boumpa, Athanasios Kakarountas 2024-04-30T11:56:16Z 置信度 0.82 computer sciencedata sciencecloud computinginternet of thingsindustry 4.0