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
- Awesome Projects: Projects Source code | Projects Articles
- Benchmarking
- Resources
- Contact & Feedback
- 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]
- 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]
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[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 |
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FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network |
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Image Classification on IoT Edge Devices: Profiling and Modeling|
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[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 |
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Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization |
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Low-Power Computer Vision: Status, Challenges, Opportunities |
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COMPRESSING RNNS FOR IOT DEVICES BY 15-38X USING KRONECKER PRODUCTS |
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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 |
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[MCUNet] Tiny Deep Learning on IoT Devices |
[pdf][official code] -
Efficient Residue Number System Based Winograd Convolution |
[pdf] -
On Front-end Gain Invariant Modeling for Wake Word Spotting |
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TOWARDS DATA-EFFICIENT MODELING FOR WAKE WORD SPOTTING |
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Accurate Detection of Wake Word Start and End Using a CNN |
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[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 |
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OPTIMIZE WHAT MATTERS: TRAINING DNN-HMM KEYWORD SPOTTING MODEL USING END METRIC |
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[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 |
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Automated facial recognition for wildlife that lack unique markings: A deep learning approach for brown bears |
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[HyNNA]: Improved Performance for Neuromorphic Vision Sensor based Surveillance using Hybrid Neural Network Architecture |
[pdf] -
The Hardware Lottery |
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MLPerf Inference Benchmark |
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MLPerf Mobile Inference Benchmark : Why Mobile AI Benchmarking Is Hard and What to Do About It |
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[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 |
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[TinyBERT] Distilling BERT for Natural Language Understanding |
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[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 |
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[I-BERT] Integer-only BERT Quantization |
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[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 |
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[LEAF] A LEARNABLE FRONTEND FOR AUDIO CLASSIFICATION |
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Enabling Large NNs on Tiny MCUs with Swapping |
[pdf] -
Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms |
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Estimating indoor occupancy through low-cost BLE devices |
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[Tiny Eats] Eating Detection on a Microcontroller |
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[DEVICETTS] A SMALL-FOOTPRINT, FAST, STABLE NETWORK FOR ON-DEVICE TEXT-TO-SPEECH |
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A 0.57-GOPS/DSP Object Detection PIM Accelerator on FPGA |
[pdf] -
Rethinking Co-design of Neural Architectures and Hardware Accelerators |
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Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks |
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[Apollo] Transferable Architecture Exploration |
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DEEP NEURAL NETWORK BASED COUGH DETECTION USING BED-MOUNTED ACCELEROMETER MEASUREMENTS |
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TapNet: The Design, Training, Implementation, and Applications of a Multi-Task Learning CNN for Off-Screen Mobile Input|
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MEMORY-EFFICIENT SPEECH RECOGNITION ON SMART DEVICES |
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SWIS - Shared Weight bIt Sparsity for Efficient Neural Network Acceleration |
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Hardware Aware Training for Efficient Keyword Spotting on General Purpose and Specialized Hardware |
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Hypervector Design for Efficient Hyperdimensional Computing on Edge Devices |
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When Being Soft Makes You Tough:A Collision Resilient Quadcopter Inspired by Arthropod Exoskeletons |
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[TinyOL] TinyML with Online-Learning on Microcontrollers |
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Quantization-Guided Training for Compact TinyML Models |
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hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices |
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Memory-Efficient, Limb Position-Aware Hand Gesture Recognition using Hyperdimensional Computing |
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Dynamically Throttleable Neural Networks(TNN) |
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A Comprehensive Survey on Hardware-Aware Neural Architecture Search |
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An Intelligent Bed Sensor System for Non-Contact Respiratory Rate Monitoring |
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Measuring what Really Matters: Optimizing Neural Networks for TinyML |
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Few-Shot Keyword Spotting in Any Language |
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DOPING: A TECHNIQUE FOR EXTREME COMPRESSION OF LSTM MODELS USING SPARSE STRUCTURED ADDITIVE MATRICES |
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[OutlierNets] Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection |
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[TENT] Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT |
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A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors |
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ADAPTIVE TEST-TIME AUGMENTATION FOR LOW-POWER CPU |
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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 |
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[µNAS] Constrained Neural Architecture Search for Microcontrollers |
[pdf] -
RaspberryPI for mosquito neutralization by power laser |
[pdf] -
Widening Access to Applied Machine Learning with TinyML |
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Using Machine Learning in Embedded Systems |
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[FRILL] A Non-Semantic Speech Embedding for Mobile Devices |
[pdf] -
Few-Shot Keyword Spotting in Any Language |
[pdf] -
MLPerf Tiny Benchmark |
[pdf] -
Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better |
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AttendSeg: A Tiny Attention Condenser Neural Network for Semantic Segmentation on the Edge |
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RANDOMNESS IN NEURAL NETWORK TRAINING:CHARACTERIZING THE IMPACT OF TOOLING |
[pdf] -
TinyML: Analysis of Xtensa LX6 microprocessor for Neural Network Applications by ESP32 SoC |
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[Keyword Transformer]: A Self-Attention Model for Keyword Spotting |
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LB-CNN: An Open Source Framework for Fast Training of Light Binary Convolutional Neural Networks using Chainer and Cupy |
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[Only Train Once]: A One-Shot Neural Network Training And Pruning Framework |
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[BEANNA]: A Binary-Enabled Architecture for Neural Network Acceleration|
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A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays |
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CLASSIFICATION OF ANOMALOUS GAIT USING MACHINE LEARNING TECHNIQUES AND EMBEDDED SENSORS |
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[MOBILEVIT]: LIGHT-WEIGHT, GENERAL-PURPOSE, AND MOBILE-FRIENDLY VISION TRANSFORMER |
[pdf] -
[MCUNetV2]: Memory-Efficient Patch-based Inference for Tiny Deep Learning |
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[LCS]: LEARNING COMPRESSIBLE SUBSPACES FOR ADAPTIVE NETWORK COMPRESSION AT INFERENCE TIME |
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Feature Augmented Hybrid CNN for Stress Recognition Using Wrist-based Photoplethysmography Sensor |
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[ANALOGNETS]: ML-HW CO-DESIGN OF NOISE-ROBUST TINYML MODELS AND ALWAYS-ON ANALOG COMPUTE-IN-MEMORY ACCELERATOR |
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[BSC]: Block-based Stochastic Computing to Enable Accurate and Efficient TinyML |
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[TiWS-iForest]: Isolation Forest in Weakly Supervised and Tiny ML scenarios |
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[RadarNet]: Efficient Gesture Recognition Technique Utilizing a Miniature Radar Sensor|
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The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoT |
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A Heterogeneous In-Memory Computing Cluster For Flexible End-to-End Inference of Real-World Deep Neural Networks |
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CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs |
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BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing |
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[UDC]: Unified DNAS for Compressible TinyML Models |
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A VM/Containerized Approach for Scaling TinyML Applications |
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A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting |
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PocketNN: Integer-only Training and Inference of Neural Networks via Direct Feedback Alignment and Pocket Activations in Pure C++ |
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[TinyMLOps]: Operational Challenges for Widespread Edge AI Adoption |
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[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 |
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TinyOdom: Hardware-Aware Efficient Neural Inertial Navigation |
[pdf]|[code] -
Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs |
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Green Accelerated Hoeffding Tree |
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tinyRadar: mmWave Radar based Human Activity Classification for Edge Computing |
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MACHINE LEARNING SENSORS |
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Evaluating Short-Term Forecasting of Multiple Time Series in IoT Environments |
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How to train accurate BNNs for embedded systems? |
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Vildehaye: A Family of Versatile, Widely-Applicable, and Field-Proven Lightweight Wildlife Tracking and Sensing Tags |
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On-Device Training Under 256KB Memory |
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DEPTH PRUNING WITH AUXILIARY NETWORKS FOR TINYML |
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[EdgeNeXt]: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications |
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Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots |
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[POET]: Training Neural Networks on Tiny Devices with Integrated Rematerialization and PagingPOET: Training Neural Networks on Tiny Devices |
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Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs |
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How to Manage Tiny Machine Learning at Scale – An Industrial Perspective |
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[SeLoC-ML]: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT|
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[IMU2Doppler]: Cross-Modal Domain Adaptation for Doppler-based Activity Recognition Using IMU Data" |
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[Tiny-HR]: Towards an interpretable machine learning pipeline for heart rate estimation on edge devices |
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[Enabling Fast Deep Learning on Tiny Energy-Harvesting IoT Devices]|
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Extremely Simple Activation Shaping for Out-of-Distribution Detection |
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A processing‑in‑pixel‑in‑memory paradigm for resource‑constrained TinyML applications |
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[tinySNN]: Towards Memory- and Energy-Efficient Spiking Neural Networks |
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[DeepPicarMicro]: Applying TinyML to Autonomous Cyber Physical Systems |
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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 |
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Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation |
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Enabling ISP-less Low-Power Computer Vision |
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Rethinking Vision Transformers for MobileNet Size and Speed |
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Neuromorphic Computing and Sensing in Space |
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Joint Data Deepening-and-Prefetching for Energy-Efficient Edge Learning |
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PreMa: Predictive Maintenance of Solenoid Valve in Real-Time at Embedded Edge-Level |
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Exploring Automatic Gym Workouts Recognition Locally On Wearable Resource-Constrained Devices |
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[MetaLDC]: Meta Learning of Low-Dimensional Computing Classifiers for Fast On-Device Adaption |
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Faster Attention Is What You Need: A Fast Self-Attention Neural Network Backbone Architecture for the Edge via Double-Condensing Attention Condensers |
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[TinyReptile]: TinyML with Federated Meta-Learning |
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[TinyProp] - Adaptive Sparse Backpropagation for Efficient TinyML On-device Learning |
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[LiteTrack] - Layer Pruning with Asynchronous Feature Extraction for Lightweight and Efficient Visual Tracking - Adaptive Sparse Backpropagation for Efficient TinyML On-device Learning |
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- TinyFederatedLearning |
[official code][presentation] - TinyML Study Group
- Arduino trash classification TinyML example
- TinyML on Arduino
- Edge AI Anomaly Detection
- Air Guitar CS249R
[presentation] - TinyML ESP32
- MagicWand-TFLite-ESP32
- Localize your cat at home with BLE beacon, ESP32s, and Machine Learning
- ESP32 Cam and Edge Impulse
- The C++ Neural Network and Machine Learning project
- Water Meter System Complete
- Number recognition with MNIST on Raspberry Pi Pico
- HallSensor RPM meter using Machine Learning
- Weather forcasting with TinyML
- TinyML using different frameworks applied to STM32F407 uC
- CurrentSense-TinyML
- Tensorflow Lite for Microcontrollers in Micropython
- TensorFlow Lite Micro for Espressif Chipsets
- ML Audio Classifier Example for Pico
- Handwritten digit classification using Raspberry Pi Pico and Machine Learning
2020-09Autonomous embedded driving using computer vision2020-10EleTect - TinyML and IoT Based Smart Wildlife Tracker2020-03Handwriting Recognition2021-01Why Benchmarking TinyML Systems Is Challenging2021-01Build your own Google Assistant using tinyML2021-02Fall detection and heart rate monitoring using AVR-IoT2021-02The Maker Show: TinyML for wildlife conservation2021-05Under $100 and Less Than 1mW: Pneumonia Detection Solution for Everyone2021-06Early Pigs' Respiratory Disease Detection Using Edge Impulse2021-06Posture Watchdog2021-07Localized Environmental Sensing With TinyML2021Wireless 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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- 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]
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[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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2019-12TinyML as-a-Service: What is it and what does it mean for the IoT Edge?2019-12TinyML as a Service and the challenges of machine learning at the edge2020-05Model Quantization Using TensorFlow Lite2020-09TinyML is breathing life into billions of devices2020-12Predictions for Embedded Machine Learning for IoT in 20212020-12Matthew Mattina: Life-Saving Models in Your Pocket2020-12Tiny four-bit computers are now all you need to train AI2021-01How predictive maintenance is changing the industrial enterprise for good2021-02What is TinyML?2021-02How AI is Taking on Sensors2021-04MLCommons™ Releases MLPerf™ Inference v1.0 Results with First Power Measurements2021-05TapLock - A bike lock with machine learning2021-05Taking Back Control2021-06Neural network architectures for deploying TinyML applications on commodity microcontrollers2021-06TinyML in MicroCosmos2021-06‘Small Data’ Are Also Crucial for Machine Learning2021-07A natively flexible 32-bit Arm microprocessor2021-07Wearable Devices Can Reduce Collision Risk in Blind and Visually Impaired People2021-09AI Inspection Using Analog Gauge as an Example
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- 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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- 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, 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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- [LPCV]: Low-Power Computer Vision Challenge |
[website]
If you have any suggestions about TinyML papers and projects, feel free to mail me :)
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