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這裡主要搜集單晶片（MCU）等級的機器學習、人工智慧、深度學習等相關研究及論文。而單板微電腦(SBC)、行動裝置或小型工業電腦等級之相關研究及論文請參考另一篇「Edge AI相關學術論文」。
註：相關論文連結不一定有提供PDF可供下載，或者必須有學術網路帳號才能下載，請自行點擊查閱。以下論文清單依發表時間(相同月份）由新到舊月份排序。目前小計496篇。
最後更新日期 : 2023/11/29
上一次更新日期 : 2022/10/20
2023(102)
Dec. 2023(1)
Nov. 2023(20)
- Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review
- Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images
- Physics-Enhanced TinyML for Real-Time Detection of Ground Magnetic Anomalies
- Energy-efficient Wireless Image Retrieval for IoT Devices by Transmitting a TinyML Model
- TinyFormer: Efficient Transformer Design and Deployment on Tiny Devices
- Smart Buildings: Water Leakage Detection Using TinyML
- Enabling Efficient Intermittent Computing on Brand New Microcontrollers via Tracking Programmable Voltage Thresholds
- Tiny machine learning on the edge: A framework for transfer learning empowered unmanned aerial vehicle assisted smart farming
- Object Detection at Edge Using TinyML Models
- An IoT Based New Platform for Teaching Tiny Machine Learning
- Machine Learning Hardware Implementation of Handwritten Digit Inference using Arduino and Ternary Output Binary Neural Network
- Development of an electrocardiographic signal classifier for bundle branch blocks, applying Tiny Machine Learning
- Advanced IoT-Based Fire and Smoke Detection System leveraging Deep Learning and TinyML
- Work-in-Progress: Micro-Accelerator-in-the-Loop Framework for MCU Integrated Accelerator Peripheral Fast Prototyping
- V-CNN: A Versatile Light CNN Structure For Image Recognition On Resources Constrained Platforms
- Enabling ImageNet-Scale Deep Learning on MCUs for Accurate and Efficient Inference
- A Comprehensive Android App Based Solution for Automated Attendance and Management in Institutions Using IoT and TinyML
- Combining Multiple tinyML Models for Multimodal Context-Aware Stress Recognition on Constrained Microcontrollers
- A review of on-device machine learning for IoT: An energy perspective
- ColabNAS: Obtaining lightweight task-specific convolutional neural networks following Occam’s razor
Oct. 2023(14)
- Optimizing IoT-Based Asset and Utilization Tracking: Efficient Activity Classification with MiniRocket on Resource-Constrained Devices
- Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devices
- Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge device
- Quantized Transformer Language Model Implementations on Edge Devices
- Study of the Complexity of CMOS Neural Network Implementations Featuring Heart Rate Detection
- ULEEN: A Novel Architecture for Ultra Low-Energy Edge Neural Networks
- Posture Guardian With Smart Muscle Strain Detection And Correction Using TINYML
- TinyMM: Multimodal-Multitask Machine Learning on Low-Power MCUs for Smart Glasses
- Enhancing Lightweight Neural Networks for Small Object Detection in IoT Applications
- AutoML for On-Sensor Tiny Machine Learning
- Industrial Visual Inspection with TinyML for High-Performance Quality Control
- Tiny Machine Learning: Progress and Futures
- Event-Driven Edge Deep Learning Decoder for Real-Time Gesture Classification and Neuro-Inspired Rehabilitation Device Control
- Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devices
Sep. 2023(5)
- A Machine Learning-oriented Survey on Tiny Machine Learning
- AoCStream: All-on-Chip CNN Accelerator with Stream-Based Line-Buffer Architecture and Accelerator-Aware Pruning
- Design and Implementation of an Internet-of-Things-Enabled Smart Meter and Smart Plug for Home-Energy-Management System
- Synergy of Patent and Open-Source-Driven Sustainable Climate Governance under Green AI: A Case Study of TinyML
- Low-cost air, noise, and light pollution measuring station with wireless communication and tinyML
Aug. 2023(6)
- TinyProp – Adaptive Sparse Backpropagation for Efficient TinyML On-device Learning
- MRQ:Support Multiple Quantization Schemes through Model Re-Quantization
- first_pagesettingsOrder Article Reprints Open AccessArticle An Efficient, Lightweight, Tiny 2D-CNN Ensemble Model to Detect Cardiomegaly in Heart CT Images
- Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres
- Gait Stride Length Estimation Using Embedded Machine Learning
- TinyML-Sensor for Shelf Life Estimation of Fresh Date Fruits
Jul. 2023(2)
- TinyMetaFed: Efficient Federated Meta-Learning for TinyML
- The Design and Optimization of an Acoustic and Ambient Sensing AIoT Platform for Agricultural Applications
Jun. 2023（6）
- U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT
- MLonMCU: TinyML Benchmarking with Fast Retargeting
- RAMAN: A Re-configurable and Sparse tinyML Accelerator for Inference on Edge
- TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers
- DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning
- Scalable Lightweight IoT-Based Smart Weather Measurement System
May 2023(8)
- Reduced Precision Floating-Point Optimization for Deep Neural Network On-Device Learning on MicroControllers
- AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing
- TinyML Design Contest for Life-Threatening Ventricular Arrhythmia Detection
- Cheshire: A Lightweight, Linux-Capable RISC-V Host Platform for Domain-Specific Accelerator Plug-In
- TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers
- Trends and Challenges in AIoT/IIoT/IoT Implementation
- Persistence Landscapes—Implementing a Dataset Verification Method in Resource-Scarce Embedded Systems
- TinyM2Net-V2: A Compact Low Power Software Hardware Architecture for Multimodal Deep Neural Networks
Apl. 2023(13)
- Multiplierless In-filter Computing for tinyML Platforms
- The Case for Hierarchical Deep Learning Inference at the Network Edge
- Device management and network connectivity as missing elements in TinyML landscape
- SSS3D: Fast Neural Architecture Search For Efficient Three-Dimensional Semantic Segmentation
- Cashew dataset generation using augmentation and RaLSGAN and a transfer learning based tinyML approach towards disease detection
- How Tiny Can Analog Filterbank Features Be Made for Ultra-low-power On-device Keyword Spotting?
- MEMA Runtime Framework: Minimizing External Memory Accesses for TinyML on Microcontrollers
- TinyReptile: TinyML with Federated Meta-Learning
- SwiftTron: An Efficient Hardware Accelerator for Quantized Transformers
- Data Aware Neural Architecture Search
- A Super-Efficient TinyML Processor for the Edge Metaverse
- DNN Is Not All You Need: Parallelizing Non-neural ML Algorithms on Ultra-low-power IoT Processors
- Constrained Tiny Machine Learning for Predicting Gas Concentration with I4.0 Low-cost Sensors
Mar. 2023(6)
- DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training
- Fused Depthwise Tiling for Memory Optimization in TinyML Deep Neural Network Inference
- FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation
- TinyML: Tools, Applications, Challenges, and Future Research Directions
- Convolutional Neural Network-Based Low-Powered Wearable Smart Device for Gait Abnormality Detection
- A Gas Leakage Detection Device Based on the Technology of TinyML
Feb. 2023(7)
- MetaLDC: Meta Learning of Low-Dimensional Computing Classifiers for Fast On-Device Adaption
- LimitAccess: on-device TinyML based robust speech recognition and age classification
- An evaluation methodology to determine the actual limitations of a TinyML-based solution
- Coding Mel Spectrogram using Keras and Tensorflow for Home Appliances Tiny Classification
- Evaluation of low-power devices for smart greenhouse development
- An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments
- Selective Sensing of Mixtures of Gases with CMOS-SOI-MEMS Sensor Dubbed GMOS
Jan. 2023(14)
- Editorial for the Special Issue on Micro and Smart Devices and Systems
- An Impact Localization Solution Using Embedded Intelligence—Methodology and Experimental Verification via a Resource-Constrained IoT Device
- Intelligent and Efficient IoT Through the Cooperation of TinyML and Edge Computing
- Developing a TinyML-Oriented Deep Learning Model for an Intelligent Greenhouse Microclimate Control from Multivariate Sensed Data
- An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments
- A TinyML Deep Learning Approach for Indoor Tracking of Assets
- Lightweight and Energy-Efficient Deep Learning Accelerator for Real-Time Object Detection on Edge Devices
- BandX : An Intelligent IoT-band for Human Activity Recognition based on TinyML
- Employing Machine Learning and IoT for Earthquake Early Warning System in Smart Cities
- On the Adoption of Modern Technologies to Fight the COVID-19 Pandemic: A Technical Synthesis of Latest Developments
- An Impact Localization Solution Using Embedded Intelligence—Methodology and Experimental Verification via a Resource-Constrained IoT Device
- RedMule: A Mixed-Precision Matrix-Matrix Operation Engine for Flexible and Energy-Efficient On-Chip Linear Algebra and TinyML Training Acceleration
- Empirical study of the modulus as activation function in computer vision applications
- Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers
- Weightless Neural Networks for Efficient Edge Inference
2022(183)
Dec. 2022(12)
- Energy consumption of on-device machine learning models for IoT intrusion detection
- Applying Azure To Automate Dev Ops For Small ML Smart Sensors
- Edge Impulse: An MLOps Platform for Tiny Machine Learning
- TCN-CUTIE: A 1036 TOp/s/W, 2.72 uJ/Inference, 12.2 mW All-Digital Ternary Accelerator in 22 nm FDX Technology
- Neuromorphic Computing and Sensing in Space
- Rethinking Vision Transformers for MobileNet Size and Speed
- In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision
- Edge Impulse: An MLOps Platform for Tiny Machine Learning
- Towards Energy-Aware Tinyml on Battery-Less Iot Devices
- TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
- Tiny Machine Learning for High Accuracy Product Quality Inspection
- Book: Machine Learning on Commodity Tiny Devices
Nov. 2022(4)
- An embedded solution for fault detection and diagnosis of photovoltaic modules using thermographic images and deep convolutional neural networks
- AutoTinyML for microcontrollers: Dealing with black-box deployability
- A review of TinyML
- PreMa: Predictive Maintenance of Solenoid Valve in Real-Time at Embedded Edge-Level
Oct. 2022(21)
- TinyML-enabled edge implementation of transfer learning framework for domain generalization in machine fault diagnosis
- Feasibility on Detecting Door Slamming towards Monitoring Early Signs of Domestic Violence
- Optimizing Random Forest Based Inference on RISC-V MCUs at the Extreme Edge
- Optimizing PhiNet architectures for the detection of urban sounds on low-end devices
- Accurate Estimation of the CNN Inference Cost for TinyML Devices
- Machine Learning for Microcontroller-Class Hardware - A Review
- TinyML-Enabled Static Hand Gesture Recognition System Based on an Ultra-Low Resolution Infrared Array Sensor and a Low-Cost AI Chip
- DeepPicarMicro: Applying TinyML to Autonomous Cyber Physical Systems
- TMM-TinyML: tensor memory mapping (TMM) method for tiny machine learning (TinyML)
- TinyML-CAM: 80 FPS image recognition in 1 kB RAM
- PROS: an efficient pattern-driven compressive sensing framework for low-power biopotential-based wearables with on-chip intelligence
- TinyRL: Towards Reinforcement Learning on Tiny Embedded Devices
- Driving an innovation contest into crisis
- Guidelines for Artifacts to Support Industry-Relevant Research on Self-Adaptation
- Real-time neural network inference on extremely weak devices: agile offloading with explainable AI
- TinyML Gamma Radiation Classifier
- A novel framework for deployment of CNN models using post-training quantization on microcontroller
- An embedded solution for fault detection and diagnosis of photovoltaic modules using thermographic images and deep convolutional neural networks
- MinUn: Accurate ML Inference on Microcontrollers
- Split Federated Learning on Micro-controllers: A Keyword Spotting Showcase
- Enabling ISP-less Low-Power Computer Vision
Sep. 2022(21)
- Is Tiny Deep Learning the New Deep Learning?
- Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors
- TinyML for UWB-radar based presence detection
- Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors
- Darkside: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training
- WiFi Sensing on the Edge: Signal Processing Techniques and Challenges for Real-World Systems
- Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML)
- An Intelligent IoT Sensing System for Rail Vehicle Running States Based on TinyML
- Embedded Machine Learning Using Microcontrollers in Wearable and Ambulatory Systems for Health and Care Applications: A Review
- Modelling Virtual Sensors for Indoor Environments with Machine Learning
- tinyCare: A tinyML-based Low-Cost Continuous Blood Pressure Estimation on the Extreme Edge
- Quantized ID-CNN for a Low-power PDM-to-PCM Conversion in TinyML KWS Applications
- Real-Time Low Power Audio Distortion Circuit Modeling: a TinyML Deep Learning Approach
- Survey and Comparison of Milliwatts Micro controllers for Tiny Machine Learning at the Edge
- Tiny TCN model for Gesture Recognition With Multi-point Low power ToF-Sensors
- Real-time Prediction Method of Remaining Useful Life Based on TinyML
- Classifying mosquito wingbeat sound using TinyML
- Spotting the Elusive Grandis impactus in the HCI Savannah
- A Tiny CNN for Embedded Electronic Skin Systems
- Free Bits: Platform-Aware Latency Optimization of Mixed-Precision Neural Networks for Edge Deployment
- FP8 Formats for Deep Learning
- A low-cost TinyML model for Mosquito Detection in Resource-Constrained Environments
Aug. 2022(19)
- DeepPicarMicro: Applying TinyML to Autonomous Cyber Physical Systems
- Faster Attention Is What You Need: A Fast Self-Attention Neural Network Backbone Architecture for the Edge via Double-Condensing Attention Condensers
- TinyML Model for Classifying Hazardous Volatile Organic Compounds Using Low-Power Embedded Edge Sensors: Perfecting Factory 5.0 Using Edge AI
- TinyOps: ImageNet Scale Deep Learning on Microcontrollers
- A TinyML Soft-Sensor for the Internet of Intelligent Vehicles
- ML Blocks: A Block-Based, Graphical User Interface for Creating TinyML Models
- Assurance of Machine Learning/TinyML in Safety-Critical Domains
- ML-HW Co-Design of Noise-Robust TinyML Models and Always-On Analog Compute-in-Memory Edge Accelerator
- Supporting AI Engineering on the IoT Edge through Model-Driven TinyML
- Automated Neural and On-Device Learning for Micro Controllers
- Neural Network Decomposition and Distribution on Multiple Microcontrollers
- TinyMLOps: Operational Challenges for Widespread Edge AI Adoption
- A Guided Task and Obstacle Alert Robot System Based on TinyML and Augmented Reality
- Multi-Complexity-Loss DNAS for Energy-Efficient and Memory-Constrained Deep Neural Networks
- An instance-based deep transfer learning approach for resource-constrained environments
- Reducing Energy Consumption and Health Hazards of Electric Liquid Mosquito Repellents through TinyML
- Developing a multi-label tinyML machine learning model for an active and optimized greenhouse microclimate control from multivariate sensed data
- PULP-TrainLib: Enabling On-Device Training for RISC-V Multi-core MCUs Through Performance-Driven Autotuning
- TinyRCE: Multipurpose Forward Learning for Resource Restricted Devices
Jul. 2022(22)
- Implementation Of Tiny Machine Learning Models On Arduino 33 BLE For Gesture And Speech Recognition
- T-RECX: Tiny-Resource Efficient Convolutional Neural Networks with Early-Exit
- An Ultra-low Power TinyML System for Real-time Visual Processing at Edge
- A TinyML-based system for gas leakage detection
- A data-stream TinyML compression algorithm for vehicular applications: a case study
- A TinyML approach to non-repudiable anomaly detection in extreme industrial environments
- TinyML Smart Sensor for Energy Saving in Internet of Things Precision Agriculture platform
- Software Engineering Approaches for TinyML based IoT Embedded Vision: A Systematic Literature Review
- Poster Abstract: Approach for Remote, On-Demand loading and Execution of TensorFlow Lite ML Models on Arduino IoT Boards
- Poster Abstract: Embedded ML Pipeline for Precision Agriculture
- iBUG: AI Enabled IoT Sensing Platform for Real-time Environmental Monitoring
- Recent Advances in Plant Diseases Detection With Machine Learning: Solution for Developing Countries
- The C-CNN model: Do we really need multiplicative synapses in convolutional neural networks?
- Custom Hardware Inference Accelerator for TensorFlow Lite for Microcontrollers
- FlashMAC: A Time-Frequency Hybrid MAC Architecture With Variable Latency-Aware Scheduling for TinyML Systems
- Towards on-board learning for harvested energy prediction
- Monitoring neurological disorders with AI-enabled wearable systems
- TinyOdom: Hardware-Aware Efficient Neural Inertial Navigation
- A Practical View on Training Neural Networks in the Edge
- ACTION: Automated Hardware-Software Codesign Framework for Low-precision Numerical Format SelecTION in TinyML
- Real Time Classification of Fruits and Vegetables Deployed on Low Power Embedded Devices Using Tiny ML
- Elements of TinyML on Constrained Resource Hardware
Jun. 2022(12)
- On-Device Training Under 256KB Memory
- OTA-TinyML: Over the Air Deployment of TinyML Models and Execution on IoT Devices
- YOLO-Based Face Mask Detection on Low-End Devices Using Pruning and Quantization
- Optimizations of Ternary Generative Adversarial Networks
- RIS-IoT: Towards Resilient, Interoperable, Scalable IoT
- Poster Abstract: Feasibility on Detecting Door Slamming towards Monitoring Early Signs of Domestic Violence
- Comparison of Two Microcontroller Boards for On-Device Model Training in a Keyword Spotting Task
- Cough Detection System using TinyML
- A Real-Time CNN-Based Lightweight Mobile Masked Face Recognition System
- Google Home, Listen: Building Helper Intelligences for Non-Verbal Sound
- A Primer for tinyML Predictive Maintenance: Input and Model Optimisation
- EtinyNet: Extremely Tiny Network for TinyML
May 2022(12)
- Green Accelerated Hoeffding Tree
- A 1036 TOp/s/W, 12.2 mW, 2.72 μJ/Inference All Digital TNN Accelerator in 22 nm FDX Technology for TinyML Applications
- Edge AI Based Autonomous UAV for Emergency Network Deployment: A Study Towards Search and Rescue Missions
- TinyFedTL: Federated Transfer Learning on Ubiquitous Tiny IoT Devices
- Noise Cleaning of ECG on Edge Device Using Convolutional Sparse Contractive Autoencoder
- On the Role of Smart Vision Sensors in Energy-Efficient Computer Vision at the Edge
- Online Stream Sampling for Low-Memory On-Device Edge Training for WiFi Sensing
- TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications
- A TinyML Soft-Sensor Approach for Low-Cost Detection and Monitoring of Vehicular Emissions
- 0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems
- Utilization of mobile edge computing on the Internet of Medical Things: A survey
- Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation
Apr. 2022(13)
- A review on TinyML: State-of-the-art and prospects
- Depth Pruning with Auxiliary Networks for TinyML
- Software Engineering Approaches for TinyML based IoT Embedded Vision: A Systematic Literature Review
- Intelligence at the Extreme Edge: A Survey on Reformable TinyML
- A Heterogeneous In-Memory Computing Cluster for Flexible End-to-End Inference of Real-World Deep Neural Networks
- Depth Pruning with Auxiliary Networks for Tinyml
- Scalable Neural Architectures for End-to-End Environmental Sound Classification
- A Fall Detection using Sound Technology Based on TinyML
- Design and Performance Evaluation of an Ultralow-Power Smart IoT Device With Embedded TinyML for Asset Activity Monitoring
- Enabling Hyperparameter Tuning of Machine Learning Classifiers in Production
- Digital twins and artificial intelligence: as pillars of personalized learning models
- Planetary digital twin: a case study in aquaculture
- Enhancing Food Supply Chain Security through the Use of Blockchain and TinyML
Mar. 2022(19)
- A Semi-Decoupled Approach to Fast and Optimal Hardware-Software Co-Design of Neural Accelerators
- TinyMLOps: Operational Challenges for Widespread Edge AI Adoption
- Distributed On-Sensor Compute System for AR/VR Devices: A Semi-Analytical Simulation Framework for Power Estimation
- An Empirical Study of Low Precision Quantization for TinyML
- Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks
- A Brain-Inspired Low-Dimensional Computing Classifier for Inference on Tiny Devices
- P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications
- Millimeter-Scale Ultra-Low-Power Imaging System for Intelligent Edge Monitoring
- Improving the Energy Efficiency and Robustness of tinyML Computer Vision using Log-Gradient Input Images
- Hardware Deployable Edge-AI Solution for Pre-screening of Oral Tongue Lesions using TinyML on Embedded Devices
- TinyML: A Systematic Review and Synthesis of Existing Research
- A review on TinyML: State-of-the-art and prospects
- Tiny Machine Learning (Tiny-ML) for Efficient Channel Estimation and Signal Detection
- TinyMLedu: The Tiny Machine Learning Open Education Initiative
- Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications
- The Scale4Edge RISC-V ecosystem
- RedMulE: a compact FP16 matrix-multiplication accelerator for adaptive deep learning on RISC-V-based ultra-low-power SoCs
- Bioformers: embedding transformers for ultra-low power sEMG-based gesture recognition
- An On-Device Learning System for Estimating Liquid Consumption from Consumer-Grade Water Bottles and Its Evaluation
Feb. 2022(9)
- tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices
- How to Manage Tiny Machine Learning at Scale: An Industrial Perspective
- A VM/Containerized Approach for Scaling TinyML Applications
- TinyM\(^2\)Net: A Flexible System Algorithm Co-designed Multimodal Learning Framework for Tiny Devices
- A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting
- BSC: Block-based Stochastic Computing to Enable Accurate and Efficient TinyML
- Heterogeneous Memory Architecture Accommodating Processing-in-Memory on SoC for AIoT Applications
- Time series analysis for temperature forecasting using TinyML
- Development of a TinyML based four-chamber refrigerator (TBFCR) for efficiently storing pharmaceutical products: Case Study: Pharmacies in Rwanda
Jan. 2022(19)
- UDC: Unified DNAS for Compressible TinyML Models
- PocketNN: Integer-only Training and Inference of Neural Networks via Direct Feedback Alignment and Pocket Activations in Pure C++
- CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs
- A Heterogeneous In-Memory Computing Cluster For Flexible End-to-End Inference of Real-World Deep Neural Networks
- Everything You wanted to Know about Smart Agriculture
- TinyML-Based Concept System Used to Analyze Whether the Face Mask Is Worn Properly in Battery-Operated Conditions
- Tiny Generative Image Compression for Bandwidth-Constrained Sensor Applications
- TinyML in Africa: Opportunities and Challenges
- Evaluating the practical limitations of TinyML: an experimental approach
- Insect biodiversity in agriculture using IoT: opportunities and needs for further research
- Imbal-OL: Online Machine Learning from Imbalanced Data Streams in Real-world IoT
- DeepQGHO: Quantized Greedy Hyperparameter Optimization in Deep Neural Networks for on-the-Fly Learning
- Audio Distress Signal Recognition in Rural and Urban Areas using a WSN consisting of Portable Resource-Constrained Devices
- Identification of Deadliest Mosquitoes Using Wing Beats Sound Classification on Tiny Embedded System Using Machine Learning and Edge Impulse Platform
- Auritus: An Open-Source Optimization Toolkit for Training and Development of Human Movement Models and Filters Using Earables
- Towards Semantic Management of On-Device Applications in Industrial IoT
- PhiNets: a scalable backbone for low-power AI at the edge
- Roadmap for edge AI: a Dagstuhl perspective
- Energy Efficient Computing Systems: Architectures, Abstractions and Modeling to Techniques and Standards
2021(88)
Dec. 2021(17)
- TinyML Platforms Benchmarking
- Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework ICCAD Special Session Paper
- Special Session: Approximate TinyML Systems: Full System Approximations for Extreme Energy-Efficiency in Intelligent Edge Devices
- A Dataset and TinyML Model for Coarse Age Classification Based on Voice Commands
- Edge AI-based Respiratory Disease Recognition from Exhaled Breath Signatures
- Intelligent Acoustic Module for Autonomous Vehicles using Fast Gated Recurrent approach
- Resource Constrained CVD Classification Using Single Lead ECG On Wearable and Implantable Devices
- Synthetic Exhaled Breath Data-Based Edge AI Model for the Prediction of Chronic Obstructive Pulmonary Disease
- ML-based data classification and data aggregation on the edge
Nov. 2021(17)
- RaScaNet: Learning Tiny Models by Raster-Scanning Images
- arXiv - Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs
- MDPI - Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs
- arXiv - TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios
- sciencedirect - TiWS-iForest: Isolation forest in weakly supervised and tiny ML scenarios
- BSC: Block-based Stochastic Computing to Enable Accurate and Efficient TinyML
- AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator
- Automated HW/SW Co-design for Edge AI: State, Challenges and Steps Ahead: Special Session Paper
- An SRAM Optimized Approach for Constant Memory Consumption and Ultra-fast Execution of ML Classifiers on TinyML Hardware
- A QKeras Neural Network Zoo for Deeply Quantized Imaging
- Implementation of Cyber Threat Intelligence Platform on Internet of Things (IoT) using TinyML Approach for Deceiving Cyber Invasion
- TinyML Benchmark: Executing Fully Connected Neural Networks on Commodity Microcontrollers
- TinyML: Current Progress, Research Challenges, and Future Roadmap
- Real-Time Activity Tracking using TinyML to Support Elderly Care
- Cartoonize Images using TinyML Strategies with Transfer Learning
- A Review of Machine Learning and TinyML in Healthcare
- Hardware/Software Co-Design for TinyML Voice-Recognition Application on Resource Frugal Edge Devices
Oct. 2021(10)
- MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning
- Micronets: Neural network architectures for deploying tinyml applications on commodity microcontrollers
- arXiv - A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays
- IEEE - A TinyML Platform for On-Device Continual Learning With Quantized Latent Replays
- TrafficNNode: Low Power Vehicle Sensing Platform for Smart Cities
- TinyFedTL: Federated Transfer Learning on Tiny Devices
- TinyML Meets IoT: A Comprehensive Survey
- Design of a Novel Neural Network Compression Method for Tiny Machine Learning
- Intermittent-Aware Neural Architecture Search
- Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps
Sep. 2021(8)
- MbedML: A Machine Learning Project for Embedded Systems
- TinyML Benchmark: Executing Fully Connected Neural Networks on Commodity Microcontrollers
- TinyOL: TinyML with Online-Learning on Microcontrollers
- Design of a Speech Anger Recognition System on Arduino Nano 33 BLE Sense
- A Microcontroller is All You Need: Enabling Transformer Execution on Low-Power IoT Endnodes
- Capacitive Sensing Based On-board Hand Gesture Recognition with TinyML
- Automated HW/SW co-design for edge AI: state, challenges and steps ahead
- On-Device Training of Machine Learning Models on Microcontrollers With a Look at Federated Learning
Jly. 2021(4)
- Supporting AI Engineering on the IoT Edge through Model-Driven TinyML
- An Unsupervised TinyML Approach Applied for Pavement Anomalies Detection Under the Internet of Intelligent Vehicles
- Integer-Only Approximated MFCC for Ultra-Low Power Audio NN Processing on Multi-Core MCUs
- LPWAN and Embedded Machine Learning as Enablers for the Next Generation of Wearable Devices
Jun. 2021(9)
- LB-CNN: An Open Source Framework for Fast Training of Light Binary Convolutional Neural Networks using Chainer and Cupy
- TinyML: Analysis of Xtensa LX6 microprocessor for Neural Network Applications by ESP32 SoC
- MLPerf Tiny Benchmark
- Widening Access to Applied Machine Learning with TinyML
- TinyML, Anomaly Detection
- An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity
- The synergy of complex event processing and tiny machine learning in industrial IoT
- An Overview of Machine Learning within Embedded and Mobile Devices–Optimizations and Applications
- An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity
May 2021(7)
- The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoT
- TinyML benchmark: Executing fully connected neural networks on commodity microcontrollers
- Comparing Industry Frameworks with Deeply Quantized Neural Networks on Microcontrollers
- Putting AI on Diet: TinyML and Efficient Deep Learning
- A TinyMLaaS Ecosystem for Machine Learning in IoT: Overview and Research Challenges
- Adaptive Traffic Control With TinyML
- Performance of deep neural networks on low-power IoT devices
Apr. 2021(6)
- AttendSeg: A Tiny Attention Condenser Neural Network for Semantic Segmentation on the Edge
- Measuring what Really Matters: Optimizing Neural Networks for TinyML
- Compiler Toolchains for Deep Learning Workloads on Embedded Platforms
- TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT
- Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles
- μNAS: Constrained Neural Architecture Search for Microcontrollers
Mar. 2021(11)
- TinyOL: TinyML with Online-Learning on Microcontrollers
- Hypervector Design for Efficient Hyperdimensional Computing on Edge Devices
- Smartphone Impostor Detection with Behavioral Data Privacy and Minimalist Hardware Support
- Quantization-Guided Training for Compact TinyML Models
- hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
- Memory-Efficient, Limb Position-Aware Hand Gesture Recognition using Hyperdimensional Computing
- An Ultra-low Power RNN Classifier for Always-On Voice Wake-Up Detection Robust to Real-World Scenarios
- SWIS – Shared Weight bIt Sparsity for Efficient Neural Network Acceleration
- Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers
- IoT-based smart triage of Covid-19 suspicious cases in the Emergency Department
- Automating Tiny ML Intelligent Sensors DevOPS Using Microsoft Azure
Feb. 2021(4)
- Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles
- TinyML for Ubiquitous Edge AI
- Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles
- Virtualizing AI at the Distributed Edge towards Intelligent IoT Applications
Jan. 2021(3)
- Rethinking Generalization in American Sign Language Prediction for Edge Devices with Extremely Low Memory Footprint
- Toward Data-Adaptable TinyML using Model Partial Replacement for Resource Frugal Edge Device
- A 0.57-GOPS/DSP Object Detection PIM Accelerator on FPGA
2020(29)
Dec. 2020(8)
- Online On-device MCU Transfer Learning
- A VM/Containerized Approach for Scaling TinyML Applications
- Resource Efficient Deep Reinforcement Learning for Acutely Constrained TinyML Devices
- Deep Learning for Compute in Memory
- Does Form Follow Function? An Empirical Exploration of the Impact of Deep Neural Network Architecture Design on Hardware-Specific Acceleration
- TTVOS: Lightweight Video Object Segmentation with Adaptive Template Attention Module and Temporal Consistency Loss
- Privacy-Preserving Inference on the Edge: Mitigating a New Threat Model
- Mini-NAS: A Neural Architecture Search Framework for Small Scale Image Classification Applications
Nov. 2020(2)
- Tiny Neural Networks for Environmental Predictions: an integrated approach with Miosix
- Starfish: resilient image compression for AIoT cameras
Oct. 2020(2)
- MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers
- TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems
Sep. 2020(4)
- Hardware Aware Training for Efficient Keyword Spotting on General Purpose and Specialized Hardware
- AttendNets: Tiny Deep Image Recognition Neural Networks for the Edge via Visual Attention Condensers
- Hardware Aware Training for Efficient Keyword Spotting on General Purpose and Specialized Hardware
- FUDGE: a frugal edge node for advanced IoT solutions in contexts with limited resources
Aug. 2020(2)
- TinySpeech: Attention Condensers for Deep Speech Recognition Neural Networks on Edge Devices
- TinyML-Enabled Frugal Smart Objects: Challenges and Opportunities
Jul. 2020(5)
- Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles
- Benchmarking TinyML Systems: Challenges and Direction
- MCUNet: Tiny Deep Learning on IoT Devices
- Bringing machine learning to the deepest IoT edge with TinyML as-a-service
- Optimizing Machine Learning Inference
for MCU:s
Jun. 2020(1)
May 2020(1)
Mar. 2020(2)
- Benchmarking TinyML Systems: Challenges and Direction
- CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices
Feb. 2020(1)
Jan. 2020(1)
2019(5)
Nov. 2019(2)
- On-Device Machine Learning: An Algorithms and Learning Theory Perspective
- Emotion Filtering at the Edge
Oct. 2019(1)
Sep. 2019(2)
2003(1)
資料來源：Google Scholar、arXiv、ResearchGate、IEEE Xplore、ACM Digital Library、MDPI、ScienceDirect、OpenReview等
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