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PITTSBURGH, June 25, 2019 (GLOBE NEWSWIRE) -- Aspinity, a pioneer in analog neuromorphic semiconductors, today announced its Reconfigurable Analog Modular Processor (RAMP) platform, an ultra-low power, analog processing platform that overcomes the power and data handling challenges in battery-operated, always-on sensing devices for consumer, smart home, Internet of Things (IoT), industrial and other markets.
The RAMP platform incorporates machine learning into an analog processor that is revolutionary in its ability to detect and classify events —such as voice, an alarm or a change in vibrational frequency or magnitude — from background noise before the data is digitized. By directly analyzing raw analog sensor data for what’s important to the application, the RAMP platform more efficiently partitions the always-on system’s power and data resources to eliminate the higher-power processing and transmission of irrelevant data. Compared to an older “digitize-first” architecture where all sensor data must be continuously digitized for event analysis, the RAMP-based “analyze-first” approach brings more intelligence to the sensor edge, reducing the power required by up to 10x and the volume of data handled by up to 100x for always-on applications such as voice-first smart speakers and wearables/hearables, always-listening smart home security devices, and industrial vibration monitoring systems.
Demand for always-on sensing devices is surging, with billions of these intelligent systems in use within just a few years. According to Juniper Research, the installed base of digital voice assistants will triple to 8 billion by 2023.i Always-on voice-first devices, such as smart speakers and wearables/hearables, are among the largest and fastest-growing market segments, with smart speakers reaching 482 million units by 2021 (according to IHS Markit) and wearables/hearables reaching 417 million hearables by 2022 (according to Juniper Research).
With device manufacturers heavily invested in the success of always-on portable sensing devices, technology developers are working to alleviate barriers to adoption. Chief among these is the short battery life that makes many always-on sensing devices unattractive to end users.
“We are at the cusp of a mass proliferation of always-listening, continuously processing devices. To reach that next level, we need to resolve the architectural issues that are deal-breakers for some applications,” said Tom Doyle, founder and CEO, Aspinity Inc. “Voice-first devices such as smart speakers and wearables/hearables, for example, ought to run for long periods of time without requiring battery recharge or they risk frustrating consumers. We’re committed to fixing this problem through an intelligent architectural approach. Our RAMP platform analyzes the incoming sound at the microphone edge to keep the wake-word engine and other digital processors in a low-power sleep state for the 80% of the time that no voice is present. Manufacturers who can offer a voice-first TV remote that runs for a year per battery change or a smart earbud that can run for an entire day without a recharge will gain a major competitive edge in the marketplace.”
Aspinity’s patented RAMP technology replicates sophisticated digital processing tasks in compact, ultra-low power, analog circuitry which supports event detection and classification from raw, unstructured analog sensor data. Leveraging the nonlinear characteristics of a small number of transistors, RAMP incorporates modular, parallel and continuously operating analog blocks that mimic the brain’s efficiency. Each of these blocks is implemented in a much smaller and more efficient programmable footprint than traditional analog circuits. The RAMP platform supports many applications by configuring the analog blocks for typical digital tasks such as signal analysis and compression, as well as more complex tasks such as feature extraction, event detection, and classification.
Programmable, Scalable, and Flexible Technology
The RAMP platform’s analog blocks can be reprogrammed with application-specific algorithms to analyze raw analog data from multiple types of sensors, such as accelerometers used for industrial vibration monitoring. Instead of a predictive maintenance system that continuously digitizes thousands of points of data to monitor the trends in the changes of certain spectral peaks, RAMP can sample and select only the most important data points, compressing the quantity of vibration data by 100x and dramatically decreasing the amount of data collected and transmitted for analysis. Reducing the amount of data that is handled in this type of always-on application is the key to a more easily deployable, battery-operated, wireless sensor system.
For more information on RAMP, visit www.aspinity.com/Technology or email Aspinity: email@example.com.
Founded in 2015 to commercialize research out of West Virginia University, Aspinity is transforming the design of intelligent sensing devices. Based on the founding technical team’s expertise in neuromorphic and computational electronics, Aspinity’s unique approach implements machine learning with analog circuitry, enabling an edge architecture that optimizes power and data usage in always-on, battery-powered sensing devices such as voice/sound wake-up, industrial machine health monitoring and wearable health monitoring in consumer, industrial and biomedical markets.
Aspinity holds the exclusive license to its patented technology and continues to independently develop new technologies that support the company’s mission to offer power- and data-efficient solutions that improve user experiences with always-on intelligent devices. The company is funded by Amazon Alexa Fund, Birchmere Ventures, Mountain State Capital and others. To learn more about Aspinity, visit www.aspinity.com
i “Digital Voice Assistants in Use to Triple to 8 Billion by 2023, Driven by Smart Home Devices” (Juniper Research, February 12, 2018)
Press Contacts Marcie Weinstein Aspinity Email: firstname.lastname@example.org Maria Vetrano Vetrano Communications Email: email@example.com