This is a list of interesting papers, projects, articles and talks about TinyML.
- 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]
- [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]
- 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] - 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] - Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization |
[pdf]
- 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] - 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] - 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] - [Larq] An Open-Source Library for Training Binarized Neural Networks |
[pdf][presentation][official code]
- [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] - [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] - 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] - 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]
- TinyFederatedLearning |
[official code][presentation] - Moving From AI To IntelligentAI To Reduce The Cost Of AI At The Edge
[official code][presentation] - Vibration Monitoring Machine Learning Demonstration
[official code][presentation] - TinyML Study Group
- Arduino trash classification TinyML example
- Air Guitar CS249R
[presentation] - TinyML ESP32
- TinyML Motion Classifier
- Edge AI Anomaly Detection
- EleTect - TinyML and IoT Based Smart Wildlife Tracker
- MagicWand-TFLite-ESP32
- Autonomous embedded driving using computer vision
- The C++ Neural Network and Machine Learning project
- TinyML on Arduino
- Water Meter System Complete
- Handwriting Recognition
- Why Benchmarking TinyML Systems Is Challenging
- Arduino Machine Learning: Build a Tensorflow lite model to control robot-car
- Build your own Google Assistant using tinyML
- Weather forcasting with TinyML
- Localize your cat at home with BLE beacon, ESP32s, and Machine Learning
- Fall detection and heart rate monitoring using AVR-IoT
- Number recognition with MNIST on Raspberry Pi Pico
- HallSensor RPM meter using Machine Learning
- The Maker Show: TinyML for wildlife conservation
- TinyML using different frameworks applied to STM32F407 uC
- ESP32 Cam and Edge Impulse
- TinyML ESP32-CAM: Edge Image classification with Edge Impulse
- CurrentSense-TinyML
- Under $100 and Less Than 1mW: Pneumonia Detection Solution for Everyone
- Early Pigs' Respiratory Disease Detection Using Edge Impulse
- Posture Watchdog
- Wireless Quarter: Edge Intelligence
- Localized Environmental Sensing With TinyML
- TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
- Model Quantization Using TensorFlow Lite
- What is TinyML?
- TinyML as-a-Service: What is it and what does it mean for the IoT Edge?
- TinyML is breathing life into billions of devices
- TinyML as a Service and the challenges of machine learning at the edge
- Predictions for Embedded Machine Learning for IoT in 2021
- Matthew Mattina: Life-Saving Models in Your Pocket
- How predictive maintenance is changing the industrial enterprise for good
- Tiny four-bit computers are now all you need to train AI
- Taking Back Control
- How AI is Taking on Sensors
- MLCommons™ Releases MLPerf™ Inference v1.0 Results with First Power Measurements
- TapLock - A bike lock with machine learning
- Neural network architectures for deploying TinyML applications on commodity microcontrollers
- A natively flexible 32-bit Arm microprocessor
- Wearable Devices Can Reduce Collision Risk in Blind and Visually Impaired People
- TinyML in MicroCosmos
- Edge Impulse
- EVE is Edge Virtualization Engine
- microTVM
- Larq
- Neural Network on Microcontroller (NNoM)
- CS249r: Tiny Machine Learning |Youtube
- Tiny Machine Learning (TinyML)|
[code] - Introduction to Embedded Machine Learning
- Embedded and Distributed AI course at Jonkoping University, Sweden
| Title | Speaker | Published Date |
|---|---|---|
| ------ | ----- | - |
If you have any suggestions about TinyML papers and projects, feel free to mail me :)