–Chip company’s neuromorphic analog chip dramatically reduces power consumption in always-on IoT and IIoT devices, smart speakers, voice-activated TV remotes, hearables
PITTSBURGH — September 2, 2020 –Aspinity, a pioneer in ultra-low-power analog machine learning processors, today announced that it has raised $5.3 million in Series A funding led by Anzu Partners, a venture capital and private equity firm focused on breakthrough industrial technologies. Other participating investors in this round include Amazon’s Alexa Fund, Birchmere Ventures, Mountain State Capital and Riverfront Ventures.
Founded in 2015, Aspinity leverages a unique class of machine learning enabled by proprietary analog circuit technologies, to dramatically improve battery life in smart electronic devices that are always on and always sensing the environment for acoustic, vibrational, or other ambient triggers. These devices are continuously consuming power while waiting for triggers such as voice to wake-up a voice-controlled TV remote or wireless earbuds, the sound of glass breaking to activate a smart home security system, or a change in vibrational frequency to provide early warning of equipment malfunction on the factory floor. While such smart devices are growing in popularity, e.g., SAR Insight & Consulting forecasts an installed base of 1 billion voice assistant devices in use by 2023, they are prone to short battery life because of the power required to continuously digitize and process all incoming sensor data, relevant or not.
“Aspinity’s mission is to help system designers solve a major pain point that users face on a daily basis — the necessity of recharging or replacing batteries way too frequently in their always-listening devices,” said Tom Doyle, founder and CEO, Aspinity. “Our new investment will allow us to speed deployment of our first neuromorphic analog-processing chip, which completely changes the way that incoming sensor data are handled. Instead of a system that immediately digitizes all data for further analysis, our chip uses near-zero power to analyze the sensor data while it is still in its native analog format, only waking the digital system when important data are detected. This makes our architecture a game-changer for manufacturers who want to make smart always-on devices with batteries that last up to 10 times longer. Just imagine wireless earbuds that last for months instead of a day on a single charge or a voice-activated TV remote that runs for years without battery replacement.”
In related news, Aspinity announced the expansion of its board of directors with the appointment of Dr. Jimmy Kan, principal at Anzu Partners.
“Until very recently, always-on sensing devices were generally too power-hungry, too data-intensive, and too costly to run on battery,” said Kan. “Aspinity is changing that paradigm with the first commercially available reconfigurable analog machine learning processors that intelligently focus the power resources of compact and connected always-on devices on data that really matter. We are excited to support them in transforming the next generation of smart IoT devices.”
“In 2017, we welcomed Aspinity to the inaugural Alexa Accelerator program, powered by Techstars, which was created to advance Alexa in voice-enabled devices,” said Paul Bernard, director, Amazon Alexa Fund. “We joined their 2018 seed round and are investing again today to help Aspinity advance its distinctive method for improving the battery life in portable Alexa devices.”
The new funding is intended to accelerate Aspinity’s product roadmap, enhance the technical and partner support teams, and scale volume production to meet customer demand.
Latest Aspinity Milestones
Aspinity recently announced two significant industry collaborations:
- In May 2020, Aspinity announced a partnership with Infineon Technologies AG that will accelerate development of intelligent sensing products with longer-lasting batteries.
- In December 2019, Aspinity used popular low-cost microcontrollers from STMicroelectronics to demonstrate the world’s most power-efficient end-to-end voice wake-up solutions for voice-first devices.
