Aspinity Puts Neural Networks Back to Analog

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eetimes.com, Jun. 25, 2019 – 

Aspinity, a Pittsburg-based startup founded in 2015, is launching Tuesday a Reconfigurable Analog Modular Processor platform, or RAMP. The ultra-low power, analog processing platform is designed to first detect, analyze and classify raw sensor data – in the analog domain. Once it distinguishes data (a voice, an alarm, a change in vibrational frequency or magnitude, etc.) from background noise, RAMP hands off the data for digitization.

The upshot of this "analyze-first-in-analog" approach is that it "reduces the power required at the edge by up to 10x and the volume of data handled by up to 100x for always-on applications," according to Aspinity. The startup claims that RAMP can play a key role in battery-operated, always-on sensing devices for consumer, smart home, IoT and industrial markets.

Mike Demler, senior analyst at The Linley Group told EE Times, "RAMP's most distinctive feature is its extreme low power. Drawing just 10 microamps during active operation is quite a feat for an analog chip."

Aspinity's founder and CEO Tom Doyle told us that he was delighted when he recently heard Gene Frantz talking about "the need to move neural networks back to analog." Frantz, previously a technology fellow and a staunch promoter of DSP at TI, is now a professor at Rich University. Earlier this year, in an interview with EE Times, he suggested that AI needs a better solution and for that, "we should consider going back to analog signal processing."

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