# Machine Learning for Microcontroller-Class Hardware: A Review

arXiv: https://arxiv.org/abs/2205.14550 · HTML: https://mlsanity.com/paper/2205.14550/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 139 of its 269 references that are in the map.
- First posted on arXiv 29 May 2022.

## Builds on (most cited first, 30 of 139)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [TinyBERT: Distilling BERT for Natural Language Understanding](https://mlsanity.com/paper/1909.10351/) (2019)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [SNIP: Single-shot Network Pruning based on Connection Sensitivity](https://mlsanity.com/paper/1810.02340/) (2018)
- [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)

## Built on by (most cited first, 28 of 28)

- [A Machine Learning-oriented Survey on Tiny Machine Learning](https://mlsanity.com/paper/2309.11932/) (2023)
- [On-device Training: A First Overview on Existing Systems](https://mlsanity.com/paper/2212.00824/) (2022)
- [Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO](https://mlsanity.com/paper/2311.01057/) (2023)
- [Flexible and Fully Quantized Ultra-Lightweight TinyissimoYOLO for Ultra-Low-Power Edge Systems](https://mlsanity.com/paper/2307.05999/) (2023)
- [Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review](https://mlsanity.com/paper/2311.11883/) (2023)
- [Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey](https://mlsanity.com/paper/2309.15467/) (2023)
- [Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors](https://mlsanity.com/paper/2408.12481/) (2024)
- [Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications](https://mlsanity.com/paper/2508.15008/) (2025)
- [Benchmarking Ultra-Low-Power μNPUs](https://mlsanity.com/paper/2503.22567/) (2025)
- [U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT](https://mlsanity.com/paper/2306.14574/) (2023)
- [From Tiny Machine Learning to Tiny Deep Learning: A Survey](https://mlsanity.com/paper/2506.18927/) (2025)
- [DSORT-MCU: Detecting Small Objects in Real-Time on Microcontroller Units](https://mlsanity.com/paper/2410.16769/) (2024)
- [Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation](https://mlsanity.com/paper/2502.12690/) (2025)
- [msf-CNN: Patch-based Multi-Stage Fusion with Convolutional Neural Networks for TinyML](https://mlsanity.com/paper/2505.11483/) (2025)
- [EdgeMark: An Automation and Benchmarking System for Embedded Artificial Intelligence Tools](https://mlsanity.com/paper/2502.01700/) (2025)
- [Robust Support Vector Machines for Imbalanced and Noisy Data via Benders Decomposition](https://mlsanity.com/paper/2503.14873/) (2025)
- [Floating-Point Neural Network Verification at the Software Level](https://mlsanity.com/paper/2510.23389/) (2025)
- [Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razor](https://mlsanity.com/paper/2212.07700/) (2022)
- [OpTC -- A Toolchain for Deployment of Neural Networks on AURIX TC3xx Microcontrollers](https://mlsanity.com/paper/2404.15833/) (2024)
- [SepAl: Sepsis Alerts On Low Power Wearables With Digital Biomarkers and On-Device Tiny Machine Learning](https://mlsanity.com/paper/2408.08316/) (2024)
- [MambaLite-Micro: Memory-Optimized Mamba Inference on MCUs](https://mlsanity.com/paper/2509.05488/) (2025)
- [FEATHer: Fourier-Efficient Adaptive Temporal Hierarchy Forecaster for Time-Series Forecasting](https://mlsanity.com/paper/2601.11350/) (2026)
- [Efficient and Accurate Downfacing Visual Inertial Odometry](https://mlsanity.com/paper/2509.10021/) (2025)
- [EdgeLDR: Quaternion Low-Displacement Rank Neural Networks for Edge-Efficient Deep Learning](https://mlsanity.com/paper/2601.05379/) (2026)
- [Towards Resilient Intrusion Detection in CubeSats: Challenges, TinyML Solutions, and Future Directions](https://mlsanity.com/paper/2604.06411/) (2026)
- [Towards Real-Time ECG and EMG Modeling on μNPUs](https://mlsanity.com/paper/2604.18067/) (2026)
- [Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras](https://mlsanity.com/paper/2607.28293/) (2026)
- [tinyDSM: A Framework for Skill Modeling and Development for Resource-Constrained Millirobots](https://mlsanity.com/paper/2608.17596/) (2026)

## Abstract (arXiv)

The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.