Raw-signal neural networks are the most energy-efficient option for embedded acoustic emission classification, beating feature-based models by 52-71x on energy because feature extraction dominates the total time.
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Comparison of Tiny Machine Learning Techniques for Embedded Acoustic Emission Analysis
Raw-signal neural networks are the most energy-efficient option for embedded acoustic emission classification, beating feature-based models by 52-71x on energy because feature extraction dominates the total time.