Embedded Machine Learning for Microcontroller-Class Edge Devices: Data, Feature, Evaluation, and Deployment Pipelines
arXiv:2606.18122v1 Announce Type: cross Abstract: Embedded machine learning moves inference from cloud services to resource-constrained devices that must acquire data, preprocess signals, run a model, and act within tight limits on memory, energy, and latency. This paper presents a systems-oriented synthesis of an embedded machine-learning workflow for microcontroller-class platforms. The emphasis is placed on engineering decisions that are often hidden in generic machine-learning introductions: sampling and buffering, feature extraction as dimensionality reduction, validation under class imba
The proliferation of AI and the increasing demand for real-time processing at the 'edge' necessitates efficient deployment on resource-constrained devices, bridging the gap between advanced models and practical applications.
This development enables a new wave of autonomous, intelligent devices, reducing reliance on centralized cloud infrastructure by performing inference locally, thereby enhancing privacy, efficiency, and robustness.
Machine learning capabilities are becoming accessible to a much broader range of hardware, moving from high-performance general-purpose computing to constrained embedded systems, enabling more distributed intelligence.
- · Embedded systems developers
- · IoT device manufacturers
- · Edge AI providers
- · Specialized semiconductor companies
- · Generic cloud AI service providers
- · Companies reliant on constant internet connectivity for AI functions
Increased adoption of AI in industrial, consumer, and defence applications where real-time, offline inferencing is critical.
Reduced data transmission and storage needs as more AI processing occurs at the source, impacting network infrastructure and data center growth.
Enhanced data privacy and security for sensitive applications as raw data does not need to leave the edge device for processing.
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Read at arXiv cs.AI