Ambiq preps processor platform with NN support
Fabless chip company Ambiq Micro Inc. (Austin, Texas), a pioneer of sub-threshold voltage design for extreme power efficiency in microcontrollers, is getting ready to announce its next-generation product family, which is being crafted to address a range of work-loads including neural networks.
Ambiq was founded in 2010 as a spin off from University of Michigan to commercialize sub-threshold voltage operation of circuits, which it has applied to families of real-time clock circuits and its Apollo1 and Apollo2 families of ARM-based microcontrollers. The Apollo microcontrollers can operate at voltages below 0.5V and the company claims that this can provide a 10-fold improvement in MCU power consumption compared with competitors' MCUs. This is mainly because power consumption scales with the square of voltage.
The company has made a particular point of addressing wearables markets where low power consumption for battery operation is a priority and, as a new market, their are fewer issues over established players using legacy to maintain position (see CEO interview: Ambiq sees broader options for low voltage). This has also exposed Ambiq to a number of customers working on machine learning applications in things like speech interfaces and keyword detection, Scott Hanson, founder and CTO of Ambiq, told eeNews Europe in a telephone interview.
"Apollo is already being used for neural networks through CMSIS libraries for Cortex-M4. It turns out sub-threshold [voltage operation] plus ARM is a good platform for machine learning," Hanson said. "We have voice solutions running on Apollo. We have partnered with DSP Concepts Inc. on beamforming and noise reduction and with Sensory Inc. on keyword detection."
In a YouTube-published webinar Hanson said that Ambiq's customers today are being limited to the use of relatively lightweight neural networks. "Neural networks can very quickly result in model sizes that are enormous and that can be a pretty big compute burden. The Cortex-M4 devices that we have today and that are prevalent in wearables are really not tuned for neural networks," he said. Hanson continued: "There is a need for something more. It's an opportunity and also the next great energy problem to solve."
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