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TinyML Paper and Projects

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]

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]
  • 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]

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]
  • 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]

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]
  • [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]

Awesome TinyML Projects

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