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Pittsburgh-based Aspinity has unveiled the first analog machine learning chip as part of its analogML family.
The chip, the AML100, is the industry’s first analog tiny machine learning solution. In practice, that means always-on system power is reduced by 95 percent.
Key features:
- Consumes less than 20µA when always-sensing
- Intelligently reduces quantity of data by up to 100x while the data are still in analog
- Features field-programmable functionality to address a wide range of always-on applications
- Leverages patented analog compression technology for preroll collection to maintain accuracy of wake word engine in voice-enabled devices
- Supports 4 analog sensors in any combination (microphones, accelerometers, etc.)
- Available in 7mm x 7mm 48-pin QFN package
Devices that previously required a wired power connection – or large battery, where viable – can use the AML100 to create new product classes and/or enable more flexible deployments.
Tom Doyle, Founder and CEO of Aspinity, said:
“We’ve long realised that reducing the power of each individual chip within an always-on system provides only incremental improvements to battery life. That’s not good enough for manufacturers who need revolutionary power improvements.
The AML100 reduces always-on system power to under 100µA, and that unlocks the potential of thousands of new kinds of applications running on battery.”
Current always-on devices continuously collect vast amounts of natively analog data and therefore consume a large amount of power to process mostly irrelevant data.
Aspinity claims the AML100 moves the machine learning workload to ultra-low-power analog “where the AML100 can determine data relevancy with a high degree of accuracy and at near-zero power.”
The AML100 is set for mass production in Q4 2022.
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