Computer Science > Machine Learning
arXiv:2606.18122 (cs)
[Submitted on 16 Jun 2026]
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 imbalance, model/runtime co-design, and streaming deployment. Two representative signal families are used throughout the paper. The first is inertial motion recognition, where a two-second, three-axis accelerometer window is transformed from raw samples into root-mean-square and spectral features before classification. The second is keyword spotting, where audio is sampled, anti-aliased, transformed into mel-frequency cepstral coefficients, and processed by a compact one-dimensional convolutional network. The paper concludes with practical design rules for robust on-device inference, including data curation, quantization, thresholding, scheduling, and field monitoring.
| Comments: | 6 pages, 3 figures, 4 tables |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Audio and Speech Processing (eess.AS); Signal Processing (eess.SP) |
| Cite as: | arXiv:2606.18122 [cs.LG] |
| (or arXiv:2606.18122v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.18122 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Mostafa Darvishi [view email]
[v1]
Tue, 16 Jun 2026 16:22:24 UTC (353 KB)
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