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QEEXO Launches Embedded Machine Learning Platform to Enable AI on Edge Devices

Qeexo, developer of machine learning and AI solutions for sensor data, today announced the launch of Qeexo Machine Learning Platform for embedded products and applications. The company also announced that its EarSense product has been named a CES 2019 Innovation Awards Honoree.

Qeexo Embedded Machine Learning is a lightweight, general-purpose platform that can perform inferencing locally, on an embedded edge device, in real-time and without relying on the cloud. The solution is built upon Qeexo's proprietary Machine Learning Platform, which already powers over 170 million smartphones and tablets worldwide with Qeexo's FingerSense and EarSense products.

"Qeexo Embedded Machine Learning can help any company make sense of the constant streams of data their products and equipment are already gathering or could be gathering," said Sang Won Lee, CEO of Qeexo. "As silicon chips continue to become more powerful and less expensive, we believe that the trend is for machine learning to move towards the edge."

Qeexo Embedded Machine Learning can add intelligence to products and processes in any industry. For example, in an industrial setting, Qeexo-powered sensors can be set up in factories to monitor and analyze processes, equipment, and products of interest, allowing machinery to function longer and more optimally. In automotive, sensors equipped with Qeexo Embedded Machine Learning can relay current road and automobile conditions for real-time response or predictive maintenance of the car itself. In smart home and IoT, edge devices can be augmented with more useful and convenient functions at a low added cost.

Features of Qeexo's Machine Learning Platform include:

  • Millisecond-Latency: Since Qeexo's millisecond-latency is faster than human perception, actions triggered by Qeexo's machine learning feel instantaneous. Traditionally, machine learning could not be used for time-sensitive applications such as touchscreens, since calculations take too long and users would be confused when a touch surface does not respond immediately. Qeexo's machine learning, as demonstrated in the FingerSense and EarSense products, can immediately differentiate between different types of touches and respond to user inputs without missing a beat.
  • Ultra-Low Power Consumption, Memory, and Processing Requirement: Embedded and mobile applications are heavily constrained by processing power, memory, and power consumption. Qeexo's Embedded Machine Learning is highly optimized, allowing for inferencing right at the edge, which results in a much wider range of possible applications.
  • Sensor Data: Qeexo's Machine Learning Platform uniquely works with data from all types of sensors. According to industry experts, over 1 trillion sensors could be deployed by 2020. Qeexo Embedded Machine Learning can leverage the vast amount data collected by those sensors, to make every device smarter and more convenient to use.

Demos

"One common complaint that we hear from companies is that they invest in collecting and storing data but they don't know how best to utilize this data," continued Lee. "Often, very primitive analysis is being done, if anything. With advanced machine learning algorithms, Qeexo Embedded Machine Learning can help companies realize the value that they are missing from sensor data."

As another testament to the strength of Qeexo's machine learning technology, Qeexo's EarSense product won the CES 2019 Innovation Awards as an Honoree in the Software and Mobile Apps category. EarSense, which launched on the OPPO Find X in July, uses state-of-the-art AI to replace hardware proximity sensors on smartphones to turn the screen off during phone calls. By eliminating the need for a physical proximity sensor, EarSense allows manufacturers to achieve true full-screen designs.

EarSense will be on display at Qeexo's booth during CES 2019, from January 8th to 11th, alongside other products powered by Qeexo's Machine Learning Platform.
Published Friday, December 07, 2018 9:39 AM by David Marshall
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