Analog Machine Learning for Always-on Edge Devices

Save battery power by eliminating the digitization and processing of irrelevant sensor data

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Use 10x Less Power for Always-Listening Voice-First

Finally – an intelligent voice wake-up solution that really listens for what’s important

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Process 100x Less Data For Vibration Monitoring

Develop smaller, lower-power predictive and preventative maintenance systems

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10

Reduction in power consumption

100

Elimination of irrelevant data processing

100

Less Data to Handle

How It Works

Discover how Aspinity's ultra-low power RAMP analog processing chip can dramatically reduce power requirements for your always-listening voice wake-up application.

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/Voice-Wake-Up

Voice

Always-listening voice wake-up with preroll support for battery-operated voice-first devices

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/acoustic-event-detection

Audio Events

Glass break, alarm, and other acoustic event detection for IoT and security applications

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/industrial-vibration-monitoring

Vibration

Vibration monitoring for preventative and predictive maintenance

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For Power Efficient Wireless Sensing, Start with Analog Processing
March 2020 In the News

For Power Efficient Wireless Sensing, Start with Analog Processing

Ed Brown of Tech Briefs writes about the benefits of early analog feature extraction for always-on sensing applications

TinyML Makes a Huge Impact on Mobile Devices
March 2020 Aspinity Blog

TinyML Makes a Huge Impact on Mobile Devices

Aspinity roundup from tinyML conference and importance of comparing system power not just chip power

Return to Analog Computing
February 2020 In the News

Return to Analog Computing

Listen to this interview with CEO Tom Doyle to hear how analog ML is changing the game for always-on sensing

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