🤖 AI Summary
This work addresses the challenge of end-to-end machine learning inference on microcontroller-class edge devices under stringent constraints on memory, energy consumption, and latency. To bridge the gap between conventional machine learning pipelines and embedded deployment realities, the authors propose a robust design framework tailored for resource-constrained environments, encompassing data acquisition, preprocessing, model compression, and streaming deployment. The framework integrates sampling buffers, feature dimensionality reduction techniques (e.g., RMS, spectral features, MFCCs), validation strategies for class imbalance, and co-optimization of models with runtime systems to form a complete embedded ML pipeline. Experimental evaluations on two representative tasks—inertial human activity recognition and keyword spotting—demonstrate that the proposed approach enables efficient, practical, and robust on-device inference, significantly narrowing the divide between general-purpose machine learning methodologies and embedded implementation requirements.
📝 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.