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TinyML Papers and Projects

TinyML is awesome.

Awesome Contributions Commits

This is a list of interesting papers, projects, articles and talks about TinyML.

Awesome Papers

2016

  • DEEP COMPRESSION: COMPRESSING DEEP NEURAL NETWORKS WITH PRUNING, TRAINED QUANTIZATION AND HUFFMAN CODING | [pdf]
  • [SQUEEZENET] ALEXNET-LEVEL ACCURACY WITH50X FEWER PARAMETERS AND <0.5MB MODEL SIZE | [pdf]

2017

  • Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference | [pdf]
  • Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things | [pdf]
  • ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices | [pdf]
  • OPENMV: A PYTHON POWERED, EXTENSIBLE MACHINE VISION CAMERA | [pdf] [official code]

2018

  • [AMC] AutoML for Model Compression and Acceleration on Mobile Devices | [pdf] [official code]

  • Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective | [pdf]

  • [HAQ] Hardware-Aware Automated Quantization with Mixed Precision | [pdf]

  • Efficient and Robust Machine Learning for Real-World Systems | [pdf]

  • [GesturePod] Gesture-based Interaction Cane for People with Visual Impairments | [pdf]

  • [YOLO-LITE] A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers | [pdf]

  • [CMSIS-NN] Efficient Neural Network Kernels for Arm Cortex-M CPUs | [pdf]

  • Quantizing deep convolutional networks for efficient inference: A whitepaper | [pdf]

  • [Hello Edge] Keyword Spotting on Microcontrollers | [pdf]

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2019

  • FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network | [pdf]

  • Image Classification on IoT Edge Devices: Profiling and Modeling| [pdf]

  • [PROXYLESSNAS] DIRECT NEURAL ARCHITECTURE SEARCH ON TARGET TASK AND HARDWARE |[pdf] [official code]

  • Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning | [pdf]

  • Visual Wake Words Dataset | [pdf]

  • Compiling KB-Sized Machine Learning Models to Tiny IoT Devices | [pdf]

  • Reconfigurable Multitask Audio Dynamics Processing Scheme | [pdf]

  • Pushing the limits of RNN Compression | [pdf]

  • A low-power end-to-end hybrid neuromorphic framework for surveillance applications | [pdf]

  • Deep Learning at the Edge | [pdf]

  • Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On Microcontrollers | [pdf] [official code]

  • [SpArSe] Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers |[pdf]

  • [MobileNetV2] Inverted Residuals and Linear Bottlenecks |[pdf]

  • Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization |[pdf]

  • Low-Power Computer Vision: Status, Challenges, Opportunities |[pdf]

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2020

  • COMPRESSING RNNS FOR IOT DEVICES BY 15-38X USING KRONECKER PRODUCTS |[pdf]

  • BENCHMARKING TINYML SYSTEMS: CHALLENGES AND DIRECTION |[pdf]

  • Lite Transformer with Long-Short Range Attention |[pdf]

  • [FANN-on-MCU] An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things |[pdf]

  • [TENSORFLOW LITE MICRO] EMBEDDED MACHINE LEARNING ON TINYML SYSTEMS |[pdf]

  • [AttendNets] Tiny Deep Image Recognition Neural Networks for the Edge via Visual Attention Condensers |[pdf]

  • [TinySpeech] Attention Condensers for Deep Speech Recognition Neural Networks on Edge Devices |[pdf]

  • Robust navigation with tinyML for autonomous mini-vehicles |[pdf] [official code]

  • [MICRONETS] NEURAL NETWORK ARCHITECTURES FOR DEPLOYING TINYML APPLICATIONS ON COMMODITY MICROCONTROLLERS |[pdf]

  • [TinyLSTMs] Efficient Neural Speech Enhancement for Hearing Aids |[pdf]

  • [MCUNet] Tiny Deep Learning on IoT Devices |[pdf] [official code]

  • Efficient Residue Number System Based Winograd Convolution | [pdf]

  • INTEGER QUANTIZATION FOR DEEP LEARNING INFERENCE: PRINCIPLES AND EMPIRICAL EVALUATION | [pdf]

  • On Front-end Gain Invariant Modeling for Wake Word Spotting | [pdf]

  • TOWARDS DATA-EFFICIENT MODELING FOR WAKE WORD SPOTTING | [pdf]

  • Accurate Detection of Wake Word Start and End Using a CNN | [pdf]

  • [PoPS] Policy Pruning and Shrinking for Deep Reinforcement Learning | [pdf]

  • Howl: A Deployed, Open-Source Wake Word Detection System | [pdf] [official code]

  • [LeakyPick] IoT Audio Spy Detector | [pdf]

  • On-Device Machine Learning: An Algorithms and Learning Theory Perspective | [pdf]

  • Leveraging Automated Mixed-Low-Precision Quantization for tiny edge microcontrollers | [pdf]

  • OPTIMIZE WHAT MATTERS: TRAINING DNN-HMM KEYWORD SPOTTING MODEL USING END METRIC | [pdf]

  • [RNNPool] Efficient Non-linear Pooling for RAM Constrained Inference | [blog] [pdf] [official code]

  • [Shiftry] RNN Inference in 2KB of RAM |[pdf]

  • [Once for All] Train One Network and Specialize it for Efficient Deployment |[pdf] [official code]

  • A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints |[pdf]

  • Rethinking Generalization in American Sign Language Prediction for Edge Devices with Extremely Low Memory Footprint |[pdf] [presentation]

  • [ShadowNet] A Secure and Efficient System for On-device Model Inference |[pdf]

  • Hardware Aware Training for Efficient Keyword Spotting on General Purpose and Specialized Hardware |[pdf]

  • Automated facial recognition for wildlife that lack unique markings: A deep learning approach for brown bears |[pdf]

  • [HyNNA]: Improved Performance for Neuromorphic Vision Sensor based Surveillance using Hybrid Neural Network Architecture |[pdf]

  • The Hardware Lottery |[pdf]

  • MLPerf Inference Benchmark |[pdf]

  • MLPerf Mobile Inference Benchmark : Why Mobile AI Benchmarking Is Hard and What to Do About It |[pdf]

  • [TinyRL] Learning to Seek: Tiny Robot Learning for Source Seeking on a Nano Quadcopter |[pdf] [presentation]

  • Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point |[pdf]

  • [TinyBERT] Distilling BERT for Natural Language Understanding |[pdf]

  • [Larq] An Open-Source Library for Training Binarized Neural Networks |[pdf] [presentation] [official code]

  • [FedML] A Research Library and Benchmark for Federated Machine Learning |[pdf]

  • Survey of Machine Learning Accelerators |[pdf]

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2021

  • [I-BERT] Integer-only BERT Quantization |[pdf]

  • [TinyTL] Reduce Memory, Not Parameters for Efficient On-Device Learning |[pdf] [official code]

  • ON THE QUANTIZATION OF RECURRENT NEURAL NETWORKS |[pdf]

  • [TINY TRANSDUCER] A HIGHLY-EFFICIENT SPEECH RECOGNITION MODEL ON EDGE DEVICES |[pdf]

  • LARQ COMPUTE ENGINE: DESIGN, BENCHMARK, AND DEPLOY STATE-OF-THE-ART BINARIZED NEURAL NETWORKS |[pdf]

  • [LEAF] A LEARNABLE FRONTEND FOR AUDIO CLASSIFICATION |[pdf]

  • Enabling Large NNs on Tiny MCUs with Swapping |[pdf]

  • Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms |[pdf]

  • Estimating indoor occupancy through low-cost BLE devices |[pdf]

  • [Tiny Eats] Eating Detection on a Microcontroller |[pdf]

  • [DEVICETTS] A SMALL-FOOTPRINT, FAST, STABLE NETWORK FOR ON-DEVICE TEXT-TO-SPEECH |[pdf]

  • A 0.57-GOPS/DSP Object Detection PIM Accelerator on FPGA |[pdf]

  • Rethinking Co-design of Neural Architectures and Hardware Accelerators |[pdf]

  • Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks |[pdf]

  • [Apollo] Transferable Architecture Exploration |[pdf]

  • DEEP NEURAL NETWORK BASED COUGH DETECTION USING BED-MOUNTED ACCELEROMETER MEASUREMENTS |[pdf]

  • TapNet: The Design, Training, Implementation, and Applications of a Multi-Task Learning CNN for Off-Screen Mobile Input|[pdf]

  • MEMORY-EFFICIENT SPEECH RECOGNITION ON SMART DEVICES |[pdf]

  • SWIS - Shared Weight bIt Sparsity for Efficient Neural Network Acceleration |[pdf]

  • Hardware Aware Training for Efficient Keyword Spotting on General Purpose and Specialized Hardware |[pdf]

  • Hypervector Design for Efficient Hyperdimensional Computing on Edge Devices |[pdf]

  • When Being Soft Makes You Tough:A Collision Resilient Quadcopter Inspired by Arthropod Exoskeletons |[pdf]

  • [TinyOL] TinyML with Online-Learning on Microcontrollers |[pdf]

  • Quantization-Guided Training for Compact TinyML Models |[pdf]

  • hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices |[pdf]

  • Memory-Efficient, Limb Position-Aware Hand Gesture Recognition using Hyperdimensional Computing |[pdf]

  • Dynamically Throttleable Neural Networks(TNN) |[pdf]

  • A Comprehensive Survey on Hardware-Aware Neural Architecture Search |[pdf]

  • An Intelligent Bed Sensor System for Non-Contact Respiratory Rate Monitoring |[pdf]

  • Measuring what Really Matters: Optimizing Neural Networks for TinyML |[pdf]

  • Few-Shot Keyword Spotting in Any Language |[pdf]

  • DOPING: A TECHNIQUE FOR EXTREME COMPRESSION OF LSTM MODELS USING SPARSE STRUCTURED ADDITIVE MATRICES |[pdf]

  • [OutlierNets] Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection |[pdf]

  • [TENT] Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT |[pdf]

  • A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors |[pdf]

  • ADAPTIVE TEST-TIME AUGMENTATION FOR LOW-POWER CPU |[pdf]

  • Compiler Toolchains for Deep Learning Workloads on Embedded Platforms |[pdf]

  • [ProxiMic] Convenient Voice Activation via Close-to-Mic Speech Detected by a Single Microphone |[pdf]

  • [Fusion-DHL] WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments |[pdf]

  • [µNAS] Constrained Neural Architecture Search for Microcontrollers |[pdf]

  • RaspberryPI for mosquito neutralization by power laser |[pdf]

  • Widening Access to Applied Machine Learning with TinyML |[pdf]

  • Using Machine Learning in Embedded Systems |[pdf]

  • [FRILL] A Non-Semantic Speech Embedding for Mobile Devices |[pdf]

  • Few-Shot Keyword Spotting in Any Language |[pdf]

  • MLPerf Tiny Benchmark |[pdf]

  • A Survey of Quantization Methods for Efficient Neural Network Inference |[pdf]

  • Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better |[pdf]

  • AttendSeg: A Tiny Attention Condenser Neural Network for Semantic Segmentation on the Edge |[pdf]

  • RANDOMNESS IN NEURAL NETWORK TRAINING:CHARACTERIZING THE IMPACT OF TOOLING |[pdf]

  • TinyML: Analysis of Xtensa LX6 microprocessor for Neural Network Applications by ESP32 SoC |[pdf]

  • [Keyword Transformer]: A Self-Attention Model for Keyword Spotting |[pdf]

  • LB-CNN: An Open Source Framework for Fast Training of Light Binary Convolutional Neural Networks using Chainer and Cupy |[pdf]

  • [Only Train Once]: A One-Shot Neural Network Training And Pruning Framework |[pdf]

  • [BEANNA]: A Binary-Enabled Architecture for Neural Network Acceleration|[pdf]

  • A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays |[pdf]

  • CLASSIFICATION OF ANOMALOUS GAIT USING MACHINE LEARNING TECHNIQUES AND EMBEDDED SENSORS |[pdf]

  • [MOBILEVIT]: LIGHT-WEIGHT, GENERAL-PURPOSE, AND MOBILE-FRIENDLY VISION TRANSFORMER |[pdf]

  • [MCUNetV2]: Memory-Efficient Patch-based Inference for Tiny Deep Learning |[pdf]

  • [LCS]: LEARNING COMPRESSIBLE SUBSPACES FOR ADAPTIVE NETWORK COMPRESSION AT INFERENCE TIME |[pdf]

  • Feature Augmented Hybrid CNN for Stress Recognition Using Wrist-based Photoplethysmography Sensor |[pdf]

  • [ANALOGNETS]: ML-HW CO-DESIGN OF NOISE-ROBUST TINYML MODELS AND ALWAYS-ON ANALOG COMPUTE-IN-MEMORY ACCELERATOR |[pdf]

  • [BSC]: Block-based Stochastic Computing to Enable Accurate and Efficient TinyML |[pdf]

  • [TiWS-iForest]: Isolation Forest in Weakly Supervised and Tiny ML scenarios |[pdf]

  • [RadarNet]: Efficient Gesture Recognition Technique Utilizing a Miniature Radar Sensor|[pdf]

  • The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoT |[pdf]

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2022

  • A Heterogeneous In-Memory Computing Cluster For Flexible End-to-End Inference of Real-World Deep Neural Networks |[pdf]

  • CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs |[pdf]

  • BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing |[pdf]

  • [UDC]: Unified DNAS for Compressible TinyML Models |[pdf]

  • A VM/Containerized Approach for Scaling TinyML Applications |[pdf]

  • A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting |[pdf]

  • PocketNN: Integer-only Training and Inference of Neural Networks via Direct Feedback Alignment and Pocket Activations in Pure C++ |[pdf]

  • [TinyMLOps]: Operational Challenges for Widespread Edge AI Adoption |[pdf]

  • [Auritus]: An Open-Source Optimization Toolkit for Training and Development of Human Movement Models and Filters Using Earables |[pdf] |[code]

  • Enabling Hyperparameter Tuning of Machine Learning Classifiers in Production |[pdf]

  • TinyOdom: Hardware-Aware Efficient Neural Inertial Navigation |[pdf] |[code]

  • Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs |[pdf]

  • Green Accelerated Hoeffding Tree |[pdf]

  • tinyRadar: mmWave Radar based Human Activity Classification for Edge Computing |[pdf]

  • MACHINE LEARNING SENSORS |[pdf]

  • Evaluating Short-Term Forecasting of Multiple Time Series in IoT Environments |[pdf]

  • How to train accurate BNNs for embedded systems? |[pdf]

  • Vildehaye: A Family of Versatile, Widely-Applicable, and Field-Proven Lightweight Wildlife Tracking and Sensing Tags |[pdf]

  • On-Device Training Under 256KB Memory |[pdf]

  • DEPTH PRUNING WITH AUXILIARY NETWORKS FOR TINYML |[pdf]

  • [EdgeNeXt]: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications |[pdf]

  • Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots |[pdf]

  • [POET]: Training Neural Networks on Tiny Devices with Integrated Rematerialization and PagingPOET: Training Neural Networks on Tiny Devices |[pdf]

  • Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs |[pdf]

  • How to Manage Tiny Machine Learning at Scale – An Industrial Perspective |[pdf

  • [SeLoC-ML]: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT|[pdf]

  • [IMU2Doppler]: Cross-Modal Domain Adaptation for Doppler-based Activity Recognition Using IMU Data" |[pdf]

  • [Tiny-HR]: Towards an interpretable machine learning pipeline for heart rate estimation on edge devices |[pdf]

  • [Enabling Fast Deep Learning on Tiny Energy-Harvesting IoT Devices]|[pdf]

  • Extremely Simple Activation Shaping for Out-of-Distribution Detection |[pdf]

  • A processing‑in‑pixel‑in‑memory paradigm for resource‑constrained TinyML applications |[pdf]

  • [tinySNN]: Towards Memory- and Energy-Efficient Spiking Neural Networks |[pdf]

  • [DeepPicarMicro]: Applying TinyML to Autonomous Cyber Physical Systems |[pdf]

  • Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors |[pdf -[Protean]: An Energy-Efficient and Heterogeneous Platform for Adaptive and Hardware-Accelerated Battery-free Computing |[pdf

  • IN-SENSOR & NEUROMORPHIC COMPUTING ARE ALL YOU NEED FOR ENERGY EFFICIENT COMPUTER VISION |[pdf]

  • Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation |[pdf]

  • Enabling ISP-less Low-Power Computer Vision |[pdf]

  • Rethinking Vision Transformers for MobileNet Size and Speed |[pdf]

  • Neuromorphic Computing and Sensing in Space |[pdf]

  • Joint Data Deepening-and-Prefetching for Energy-Efficient Edge Learning |[pdf]

  • PreMa: Predictive Maintenance of Solenoid Valve in Real-Time at Embedded Edge-Level | pdf]

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2023

  • Exploring Automatic Gym Workouts Recognition Locally On Wearable Resource-Constrained Devices |[pdf]

  • [MetaLDC]: Meta Learning of Low-Dimensional Computing Classifiers for Fast On-Device Adaption |[pdf]

  • Faster Attention Is What You Need: A Fast Self-Attention Neural Network Backbone Architecture for the Edge via Double-Condensing Attention Condensers |[pdf]

  • [TinyReptile]: TinyML with Federated Meta-Learning |[pdf

  • [TinyProp] - Adaptive Sparse Backpropagation for Efficient TinyML On-device Learning |[pdf

  • [LiteTrack] - Layer Pruning with Asynchronous Feature Extraction for Lightweight and Efficient Visual Tracking - Adaptive Sparse Backpropagation for Efficient TinyML On-device Learning |[pdf

  • [MCUFormer] - Deploying Vision Transformers on Microcontrollers with Limited Memory |[pdf

2024

  • Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences | [pdf]

  • TinyAgent: Function Calling at the Edge | [pdf]

  • [SENSORLLM]: ALIGNING LARGE LANGUAGE MODELS WITH MOTION SENSORS FOR HUMAN ACTIVITY RECOGNITION | [pdf]

  • [Penetrative AI]: Making LLMs Comprehend the Physical World | [pdf]

  • [MobileCLIP]: Fast Image-Text Models through Multi-Modal Reinforced Training | [pdf]

  • [Zero-TPrune]: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers | [pdf]

  • Towards Edge General Intelligence via Large Language Models: Opportunities and Challenges | [pdf]

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Awesome TinyML Projects

Projects Source code

Projects Articles

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Benchmarking and Others

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Books

  • [2022-12] AI at the Edge (D. Situnayake & J. Plunkett, 2022. O'Reilly): [Book]
  • [2022-10] Machine Learning on Commodity Tiny Devices (S. Guo & Q. Zhou, 2022. CRC Press): [Book]
  • [2022-07] Introduction to TinyML (Rohit Sharma, 2022, AITS): [Book] | [GitHub]
  • [2022-04] TinyML Cookbook (Gian Marco Iodice, 2022. Packt): [Book] | [GitHub]
  • [2021-03] Artificial Intelligence for IoT Cookbook (Michael Roshak, 2021. Packt): [Book] | [GitHub]
  • [2020-04] Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter: Build scalable real-world projects to implement end-to-end neural networks on Android and iOS (Anubhav Singh, Rimjhim Bhadani, 2020. Packt): [Book]
  • [2020-01] TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers (Pete Warden. O'Reilly Media): [Book]
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Articles

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Libraries and Tools

  • Edge Impulse - Interactive platform to generate models that can run in microcontrollers. They are also quite active on social netwoks talking about recent news on EdgeAI/TinyML.
  • EVE is Edge Virtualization Engine
  • microTVM - is an open source tool to optimize tensor programs.
  • Larq - An Open-Source Library for Training Binarized Neural Networks.
  • Neural Network on Microcontroller (NNoM) - Higher-level layer-based Neural Network library specifically for microcontrollers. Support for CMSIS-NN.
  • BerryNet - Deep learning gateway on Raspberry Pi and other edge devices.
  • Rune - provides containers to encapsulate and deploy edgeML pipelines and applications.
  • Onnxruntime - cross-platform, high performance ML inferencing and training accelerator.
  • deepC - vendor independent TinyML deep learning library, compiler and inference framework microcomputers and micro-controllers
  • deepC for Arduino - TinyML deep learning library customized for Arduiono IDE
  • emlearn - Machine learning for microcontroller and embedded systems. Train in Python, then do inference on any device with a C99 compiler.
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Courses

  • 11-767: On-Device Machine Learning Fall - by CMU | [website]
  • TinyML4D: UNIFEI-IESTI01-TinyML-2023.1 - by UNIFEI | [website]
  • Introduction to Embedded Deep Learning - by CMU | [website]
  • TinyML and Efficient Deep Learning - by MIT | [website]
  • Machine Learning at the Edge on Arm: A Practical Introduction - by ARM | [edx]
  • CS249r: Tiny Machine Learning (TinyML) - Harvard University by Vijay Janapa Reddi: sites.google.com | [YouTube] | [edx] | [GitHub]
  • MLOps for Scaling TinyML - Harvard University by Vijay Janapa Reddi: [edX]
  • Introduction to Embedded Machine Learning - Edge Impulse by Shawn Hymel: [Coursera]
  • Embedded and Distributed AI - Jonkoping University, Sweden by Beril Sirmacek: [YouTube]
  • MLT Artificial Intelligence - EdgeAI - Machine Learning Tokyo: [YouTube]
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TinyML Talks & Conferences

Title Speaker Published Date Link
Challenges for Large Scale Deployment of Tiny ML Devices G. Raghavan 2022-04-29 slide
Building data-centric AI tooling for embedded engineers D. Situnayake 2022-04-29 slide
Sensors and ML: waking smarter for less A. Ataya 2022-05-04 slide
MLOps for TinyML: Challenges & Directions in Operationalizing TinyML at Scale V.J. Reddi 2022-05-24 slide
Vibration Monitoring Machine Learning Demonstration J. Edwards 2020-12-22 github
Moving From AI To IntelligentAI To Reduce The Cost Of AI At The Edge J. Edwards 2020-12-22 web
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Competitions

  • [LPCV]: Low-Power Computer Vision Challenge |[website]

Other Awesome Repos

Contact & Feedback

If you have any suggestions about TinyML papers and projects, feel free to mail me :)