ArticleIEEE sensors journal2022
Machine Learning for Microcontroller-Class Hardware: A Review.
Article in IEEE sensors journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Wearable Flexible Sensors for Cardiovascular Disease Monitoring.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Development and validation of a sensor-integrated smart driving test system for automated, scalable, and objective driver performance evaluation.Scientific reports · 2026Article
- PhysioKey: Edge-AI-Driven Physiological Key Agreement for Secure Body Area Networks.Sensors (Basel, Switzerland) · 2026Article
- A Cloud-Aware Scalable Architecture for Distributed Edge-Enabled BCI Biosensor System.Biosensors · 2026Article
- Atrial Fibrillation Detection on the Embedded Edge: Energy-Efficient Inference on a Low-Power Microcontroller.Sensors (Basel, Switzerland) · 2025Article
- Self-Sensing of Piezoelectric Micropumps: Gas Bubble Detection by Artificial Intelligence Methods on Limited Embedded Systems.Sensors (Basel, Switzerland) · 2025Article
- Empowering voice assistants with TinyML for user-centric innovations and real-world applications.Scientific reports · 2025Review
- Detecting eavesdropping nodes in the power Internet of Things based on Kolmogorov-Arnold networks.PloS one · 2025Article
- CRISP: a correlation-filtered recursive feature elimination and integration of SMOTE pipeline for gait-based Parkinson's disease screening.Frontiers in computational neuroscience · 2025Article
- Image Processing Hardware Acceleration-A Review of Operations Involved and Current Hardware Approaches.Journal of imaging · 2024Review
- LocoMote: AI-driven Sensor Tags for Fine-Grained Undersea Localization and Sensing.IEEE sensors journal · 2024Article
- TinyNS: Platform-Aware Neurosymbolic Auto Tiny Machine Learning.ACM transactions on embedded computing systems : TECS · 2024Article
- Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.Sensors (Basel, Switzerland) · 2024Article
- A Flexible, Large-Scale Sensing Array with Low-Power In-Sensor Intelligence.Research (Washington, D.C.) · 2024Article
- Identifying risk of stillbirth using machine learning.American journal of obstetrics and gynecology · 2023Article
- Comparison of the Usability of Apple M2 and M1 Processors for Various Machine Learning Tasks.Sensors (Basel, Switzerland) · 2023Article
- Inertial Navigation on Extremely Resource-Constrained Platforms: Methods, Opportunities and Challenges.IEEE/ION Position Location and Navigation Symposium : [proceedings]. IEEE/ION Position Location and Navigation Symposium · 2023Article
- E-prop on SpiNNaker 2: Exploring online learning in spiking RNNs on neuromorphic hardware.Frontiers in neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.