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Tom Doyle

Tom Doyle brings over 30 years of experience in operational excellence and executive leadership in analog and mixed-signal semiconductor technology to Aspinity. Prior to Aspinity, Tom was group director of Cadence Design Systems' analog and mixed-signal IC business unit, where he managed the deployment of the company's technology to the world's foremost semiconductor companies. Previously, Tom was founder and president of the analog/mixed-signal software firm, Paragon IC solutions, where he was responsible for all operational facets of the company including sales and marketing, global partners/distributors, and engineering teams in the US and Asia. Tom holds a B.S. in Electrical Engineering from West Virginia University and an MBA from California State University, Long Beach.

Want to Reduce Power in Always-on IoT Devices? Analyze First (2020)

Status: Available Now

Hundreds of millions of portable smart speakers are listening for a wake word. Millions more acoustic event-detection devices are listening for window breaks, baby cries or dog barks. Consumers appreciate how easy it is to use their always-on listening devices – but the battery drain that results from continuously processing all sounds in their environment? Not so much. 

The problem is that this massive number of battery-powered IoT devices are notoriously power-inefficient in the way that they handle sound data. Relying on the age-old “digitize-first” system architecture, these devices digitize all the incoming sensor data as soon as they enter the device; then the data are processed for relevance, and in some cases, sent to the cloud for further analysis and verification. Since 80-90% of all sound data are irrelevant in most always-listening IoT devices, the digitize-first approach wastes significant battery life.

This session will show attendees how an “analyze first” edge architecture that uses analogML at the front end of an always-listening device eliminates the wasteful digitization and processing of irrelevant data, to deliver unprecedented power-saving and data efficiency in IoT devices. 

Session attendees will:

  • Understand that while most of today’s machine learning is implemented digitally, machine learning can also be implemented in ultra-low-power programmable analog blocks (analogML) so that feature extraction and classification can be performed on a sensor’s native analog data.  
  • Understand that the power problem for IoT devices is really a problem of the device treating all data as equally important and that determining which data are important earlier in the signal chain — while the data are still analog — reduces the amount of data that are processed through higher-power digital components. This approach saves up to 10x in system power in IoT devices.
  • Learn how to integrate this new analogML edge architecture with sensors and MCUs from leading semiconductor suppliers into current and next-generation IoT devices.

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