2020 >

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

Tom Doyle- Watch Now - Duration: 27:06

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.
italicssurround text with
boldsurround text with
**two asterisks**
or just a bare URL
surround text with
strikethroughsurround text with
~~two tilde characters~~
prefix with

Score: 0 | 3 years ago | no reply

Thank you for the presentation!

Score: 0 | 3 years ago | no reply

Compelling case for power reduction using Analog. Great presentation!

Score: 1 | 3 years ago | no reply

Thanks for presenting! It's interesting to learn that voice activated devices always need to be recording and that it uses the time before the wake word to improve its performance. I'll have to look at compression on device, especially for LTE devices. Thanks

Score: 0 | 3 years ago | no reply

Very interesting presentation, thank you very much.