tinyML Summit 2022: AnalogML: Analog Inferencing for System-Level Power Efficiency
tinyML Summit 2022
tinyML Audio Session
AnalogML: Analog Inferencing for System-Level Power Efficiency
David GRAHAM, Co-founder and Chief Science Officer, Aspinity Inc.
In always-listening edge applications, the audio events of interest can occur unpredictably and/or infrequently. To ensure an event is not missed, significant power is wasted by needing to continuously digitize and process all sensor information – even though most is irrelevant noise. We will describe an innovative approach to eliminating this power inefficiency by using analog machine learning (analogML) to perform inferencing of raw, analog sensor data to determine relevancy prior to digitization. By using an extensive library of software-configurable analog circuits that can be programmed with standard machine learning techniques, analogML enables highly discriminating, ultra-low power audio event detection. Downstream digital systems can remain in sleep mode until an event has occurred and/or if further processing is needed, further saving power. AnalogML brings more intelligence into the analog domain, which results in new voice-first devices, acoustic-based security systems, and other always-listening edge devices that benefit from dramatically improved battery life.
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