Founded in 2015, the West Virginia startup says its RAMP “analyze-first” edge-architecture drastically minimizes data handling, cutting the volume of data handled by up to 100x for always-on applications while maximizing battery life thanks to a 10x power reduction.
eeNews Europe caught up with Tom Doyle, Aspinity’s CEO and founder to learn more about the company’s unique technology.
“Today, a lot of processing goes from the cloud back to the edge, but it is still challenging to get the required performance, accuracy and long battery life” explained Doyle, referring to battery-operated always-listening smart home security devices, voice-first smart speakers and wearables, and industrial vibration monitoring systems. The reason, he argues, is that these systems are digitizing too much irrelevant data. So the idea is to leverage an analog neural network onboard an analog processor to sort out and identify the data that would deserve to be digitized (in a simplified format) for subsequent use by an application processor. This way, the application processor and other components can stay off most of the time.
“We have a purely analog processor, all of the sensor data classification is done within the analog domain” the CEO emphasizes, noting that his company has developed libraries of AI algorithms based on training data, which it can then load into its RAMP chip. This means the same chip can be updated precisely for different voice detection patterns, using the same hardware.