Tiny ML

ML: Machine Learning

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Overview

TinyML studies how to run useful machine-learning tasks directly on microcontrollers and other resource-constrained edge devices, often without reliable connectivity. Common approaches include quantization, pruning, distillation, compact architectures, hardware-aware neural architecture search, and efficient feature extraction, with designs measured for latency, memory, and energy on target hardware. Applications include audio and gesture recognition, anomaly detection, embedded vision, and autonomous control, where local processing can reduce communication needs and support timely responses. The central challenge is balancing accuracy and robustness against hardware limits, while making benchmarks reflect real devices, data shifts, and deployment conditions.

Latest papers

CardsList

49 papers1 paper in the last four weeks, down 80% on the four weeks before. 0.0% of all new papers.

  1. WebSerial Vision Training for Microcontrollers: A Browser-Based Companion to On-Device CNN Training

    Apr 20, 2026Jeremy EllisTiny MLEdge ML

    High School Robotics Educator British Columbia, Canada

  2. UCell: rethinking generalizability and scaling of bio-medical vision models

    Mar 31, 2026Nicholas Kuang, Vanessa Scalon, Ji YuImage SegmentationTiny ML

    Vanderbilt University, School of Medicine, Nashville, TN · UConn Health, Center for Regenerative Medicine and Skeletal Development, Farmington CT · UConn Health, Center for Cell Analysis and Modeling, Farmington CT

  3. K-Means Based TinyML Anomaly Detection and Distributed Model Reuse via the Distributed Internet of Learning (DIoL)

    Mar 28, 2026Abdulrahman Albaiz, Fathi AmsaadTiny MLAnomaly Detection

    Department of Computer Science & Engineering Wright State University Dayton, Ohio, USA

  4. Fully Autonomous Z-Score-Based TinyML Anomaly Detection on Resource-Constrained MCUs Using Power Side-Channel Data

    Mar 28, 2026Abdulrahman Albaiz, Fathi AmsaadAnomaly DetectionTiny ML

    Department of Computer Science & Engineering, Wright State University, Dayton, Ohio, USA

  5. TinyML for Acoustic Anomaly Detection in IoT Sensor Networks

    Mar 27, 2026Amar Almaini, Jakob Folz, Ghadeer AshourAnomaly DetectionTiny ML

    computer science, Deggendorf Institute of Technology, Deggendorf, Germany · Computer Science and Engineering, University of West Bohemia, Pilsen, Czech Republic · The Applied College, King Abdulaziz University, Jeddah, Saudi Arabia

  6. Once-for-All Channel Mixers (HYPERTINYPW): Generative Compression for TinyML

    Mar 26, 2026Yassien ShaalanNeural Network CompressionTiny ML

  7. Energy Efficient Software Hardware CoDesign for Machine Learning: From TinyML to Large Language Models

    Mar 24, 2026Mohammad Saleh Vahdatpour, Yanqing ZhangPower-Efficient InferenceEnergy-Efficient ML

    Department of Computer Science Georgia State University Atlanta, GA, USA

  8. TinyML Enhances CubeSat Mission Capabilities

    Mar 20, 2026Luigi Capogrosso, Michele MagnoOn-Device InferenceEnergy-Efficient ML

    Interdisciplinary Transformation University of Austria · ETH Zurich

  9. PrototypeNAS: Rapid Design of Deep Neural Networks for Microcontroller Units

    Mar 16, 2026Mark Deutel, Simon Geis, Axel PlingeNeural Architecture SearchHardware-Aware NAS

    Fraunhofer Institute for Integrated Circuits, Fraunhofer IIS, Germany

  10. Affordable Precision Agriculture: A Deployment-Oriented Review of Low-Cost, Low-Power Edge AI and TinyML for Resource-Constrained Farming Systems

    Mar 16, 2026Riya Samanta, Bidyut SahaEdge MLTiny ML

    Techno India University, Salt Lake, West Bengal, India · Sister Nivedita University, Newtown, West Bengal, India

  11. TinyNav: End-to-End TinyML for Real-Time Autonomous Navigation on Microcontrollers

    Mar 10, 2026Pooria Roy, Nourhan Jadallah. Tomer Lapid, Shahzaib Ahmad +2Robot NavigationOn-Device Inference

    Queen’s University

  12. Scaling Laws in the Tiny Regime: How Small Models Change Their Mistakes

    Mar 7, 2026Mohammed Alnemari, Rizwan Qureshi, Nader BegrazadahNeural Scaling LawsTiny ML

    Faculty of Computer Science and Information Technology, University of Tabuk, Tabuk, Saudi Arabia · AIST Research Center, University of Tabuk, Tabuk, Saudi Arabia · Department of Computer Science, Salim Habib University, Karachi, Pakistan +1

  13. Tiny, Hardware-Independent, Compression-based Classification

    Mar 6, 2026Charles Meyers, Aaron MacSween, Erik Elmroth +1Kernel MethodsData-Efficient Learning

    Institute for Experiential AI, Northeastern University, Boston, United States · Department of Computer Science, Umeå University, Umeå, Sweden

  14. InstMeter: An Instruction-Level Method to Predict Energy and Latency of DL Model Inference on MCUs

    Mar 4, 2026Hao Liu, Qing Wang, Marco ZunigaEfficient Neural Network InferenceTiny ML

    Delft University of Technology, Delft, The Netherlands

  15. Tiny-DroNeRF: Tiny Neural Radiance Fields aboard Federated Learning-enabled Nano-drones

    Mar 2, 2026Ilenia Carboni, Elia Cereda, Lorenzo Lamberti +3Neural Radiance Fields3D Reconstruction

    Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Switzerland. · Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy. · Integrated Systems Laboratory, ETH Zürich, Switzerland.

  16. TinyVLM: Zero-Shot Object Detection on Microcontrollers via Vision-Language Distillation with Matryoshka Embeddings

    Feb 24, 2026Bibin WilsonObject DetectionOpen-Vocabulary Object Detection

    Independent Researcher

  17. Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring

    Feb 19, 2026Nina Brolich, Simon Geis, Maximilian Kasper +3Bird Vocalization ClassificationEnergy-Efficient ML

    Fraunhofer Institute for Integrated Circuits IIS, Germany · Fachhochschule Erfurt, Germany · Universität Erfurt, Germany +2

  18. Pareto Optimal Benchmarking of AI Models on ARM Cortex Processors for Sustainable Embedded Systems

    Feb 19, 2026Pranay Jain, Maximilian Kasper, Göran Köber +3Energy-Efficient MLTiny ML

    Fraunhofer Institute for Integrated Circuits IIS, Germany · Intelligent Embedded Systems (IES) - Lab, University of Freiburg, Germany · Hahn-Schickard, Germany +1

  19. Energy Overhead in Sensor Nodes: Embedded Machine Learning and Data Offloading via Low Power Wide Area Networks

    Oct 28, 2025Benjamin Karic, Nina Herrmann, Jan Stenkamp +3Internet of ThingsEnergy-Efficient ML

    University of Münster · re:edu GmbH & Co. KG