Awesome

TinyML Papers and Projects

TinyML is awesome.

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

Awesome Papers: 2016 | 2017 | 2018 | 2019 | 2020 | 2022 | 2023 | 2024
Awesome Projects: Projects Source code | Projects Articles
Benchmarking
Resources
Contact & Feedback

Awesome Papers

<ins>2016</ins>

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]

<ins>2017</ins>

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]

<ins>2018</ins>

[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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<ins>2019</ins>

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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<ins>2020</ins>

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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<ins>2021</ins>

[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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<ins>2022</ins>

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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<ins>2023</ins>

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

<ins>2024</ins>

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

<ins>Projects Source code</ins>

<ins>Projects Articles</ins>

2020-09 Autonomous embedded driving using computer vision
2020-10 EleTect - TinyML and IoT Based Smart Wildlife Tracker
2020-03 Handwriting Recognition
2021-01 Why Benchmarking TinyML Systems Is Challenging
2021-01 Build your own Google Assistant using tinyML
2021-02 Fall detection and heart rate monitoring using AVR-IoT
2021-02 The Maker Show: TinyML for wildlife conservation
2021-05 Under $100 and Less Than 1mW: Pneumonia Detection Solution for Everyone
2021-06 Early Pigs' Respiratory Disease Detection Using Edge Impulse
2021-06 Posture Watchdog
2021-07 Localized Environmental Sensing With TinyML
2021 Wireless Quarter: Edge Intelligence
Arduino Machine Learning: Build a Tensorflow lite model to control robot-car
TinyML ESP32-CAM: Edge Image classification with Edge Impulse
Predictive Maintenance with TinyAutomator
TinyML Person Detection with Arduino and Arducam

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Benchmarking and Others

EEMBCs EnergyRunner: The EEMBC EnergyRunner application framework for the MLPerf Tiny benchmark.
MLPerf - Tiny: is an ML benchmark suite for extremely low-power systems such as microcontrollers. [GitHub]
FedML: A Research Library and Benchmark for Federated Machine Learning. [GitHub]
FogML: A Research Library for source code generation of the inferencing functions for embedded devices [GitHub]
Benchmarking Machine Learning on the Edge

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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

2019-12 TinyML as-a-Service: What is it and what does it mean for the IoT Edge?
2019-12 TinyML as a Service and the challenges of machine learning at the edge
2020-05 Model Quantization Using TensorFlow Lite
2020-09 TinyML is breathing life into billions of devices
2020-12 Predictions for Embedded Machine Learning for IoT in 2021
2020-12 Matthew Mattina: Life-Saving Models in Your Pocket
2020-12 Tiny four-bit computers are now all you need to train AI
2021-01 How predictive maintenance is changing the industrial enterprise for good
2021-02 What is TinyML?
2021-02 How AI is Taking on Sensors
2021-04 MLCommons™ Releases MLPerf™ Inference v1.0 Results with First Power Measurements
2021-05 TapLock - A bike lock with machine learning
2021-05 Taking Back Control
2021-06 Neural network architectures for deploying TinyML applications on commodity microcontrollers
2021-06 TinyML in MicroCosmos
2021-06 ‘Small Data’ Are Also Crucial for Machine Learning
2021-07 A natively flexible 32-bit Arm microprocessor
2021-07 Wearable Devices Can Reduce Collision Risk in Blind and Visually Impaired People
2021-09 AI Inspection Using Analog Gauge as an Example

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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

TinyML Talks, Summit & Research Symposium: [Website] | [YouTube]
Embedded Vision Summit - Edge AI & Vision Alliance: [Website] | [YouTube]
Low-Power Computer Vision Challenge (LPCV): [Website] | [YouTube]

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

Awesome Human Activity Recognition

Contact & Feedback

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