A novel matrix-multiplying ADC enables direct machine-learning classification during data conversion, potentially reducing computational overhead for applications like ECG arrhythmia detection.
May enable low-power real-time arrhythmia detection; leaves open prospective clinical validation of MMADC classifiers.
Embedded sensing systems conventionally perform A-to-D conversion followed by signal analysis. In many applications, the analysis of interest is inference (e.g., classification), but the sensor signals involved are too complex to model analytically. Machine learning is gaining prominence because it enables data-driven training of classifiers, overcoming the need for analytical models. This work presents: 1) an algorithmic formulation, where feature extraction and classification are combined into a single matrix, reducing the total multiplications needed, and 2) a matrix-multiplying ADC (MMADC) that enables multiplication of input samples by a programmable matrix. Thus, the MMADC combines feature extraction and classification with data conversion, mitigating the need for further computations. Two systems are demonstrated: an ECG-based cardiac-arrhythmia detector and an image-pixel-based gender detector.
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Zhang et al. (2015) studied this question.
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