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AnalogML - Analog Inferencing for System-Level Power Efficiency

tinyML via YouTube

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This talk explains how configurable analog circuits perform machine-learning inference on raw audio before digitization, enabling ultra-low-power detection of glass breaks and voice activity in always-listening edge devices.

Syllabus

AnalogML: Analog Inferencing for System-Level Power Efficiency
Today's Sensor Processing at the Edge is inefficient
Shifting the ML Workload to Analog
Efficiency with Analog
AnalogMLTM: Configurable Computing Chip
Analog Neural Network
Example of a Simple AnalogMLT Audio Chain
Application: Glass Break Detection
Application: Voice Activity Detection
Application: VAD + Preroll for WWE
Conclusion

Taught by

tinyML

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