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Arm’s Startup Day Shows its Support for Future Hardware Startups

All About Circuits 18 August 2021

Finding new tech often means looking at startups. To support this critical aspect of the tech industry, Arm launches its “Startup Day” event. What is it, and what were some key takeaways?

Read the full article here: https://www.allaboutcircuits.com/news/arms-startup-day-shows-its-support-for-future-hardware-startups/

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IoT News of the Week for July 9, 2021

Stacey on IOT 12 July 2021

Anyone can now build smarter sensors using Qeexo and STMicroelectronics sensors: This is a cool example of ML at the edge. Chip vendor ST Microelectronics is working with Qeexo, a startup that builds software to easily train and generate machine learning algorithms, to ensure that its sensors will easily work with Qeexo software.

Read the full article here: https://staceyoniot.com/iot-news-of-the-week-for-july-9-2021/

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Qeexo adds AutoML to STMicro MLC sensors to speed tinyML, IIoT development

Fierce Electronics 08 July 2021

Machine learning developer Qeexo and semiconductor STMicroelectronics have teamed up to allow STMicro’s machine learning core sensors to leverage Qeexo’s AutoML automated machine learning platform that accelerates the development of tinyML models for edge devices.

Read the full article here: https://www.fierceelectronics.com/iot-wireless/qeexo-adds-automl-to-stmicro-mlc-sensors-to-speed-tinyml-iiot

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Machine-learning capable motion sensors intended for IoT

New Electronics

Qeexo, developer of the Qeexo AutoML automated machine-learning (ML) platform, and STMicroelectronics have announced the availability of ST’s machine-learning core (MLC) sensors on Qeexo AutoML.

Read the full article here: https://www.newelectronics.co.uk/electronics-news/iot-machine-learning-capable-motion-sensors/238717/

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Qeexo and STMicroelectronics Speed Development of Next-Gen IoT Applications with Machine-Learning Capable Motion Sensors

Qeexo / STMicroelectronics 07 July 2021

Qeexo and STMicroelectronics Speed Development of Next-Gen IoT Applications with Machine-Learning Capable Motion Sensors


Mountain View, CA and Geneva, Switzerland, July 7, 2021

Qeexo, developer of the Qeexo AutoML automated machine-learning (ML) platform that accelerates the development of tinyML models for the Edge, and STMicroelectronics (NYSE: STM), a global semiconductor leader serving customers across the spectrum of electronics applications, today announced the availability of ST’s machine-learning core (MLC) sensors on Qeexo AutoML.

By themselves, ST’s MLC sensors substantially reduce overall system power consumption by running sensing-related algorithms, built from large sets of sensed data, that would otherwise run on the host processor. Using this sensor data, Qeexo AutoML can automatically generate highly optimized machine-learning solutions for Edge devices, with ultra-low latency, ultra-low power consumption, and an incredibly small memory footprint. These algorithmic solutions overcome die-size-imposed limits to computation power and memory size, with efficient machine-learning models for the sensors that extend system battery life.

“Delivering on the promise we made recently when we announced our collaboration with ST, Qeexo has added support for ST’s family of machine-learning core sensors on Qeexo AutoML,” said Sang Won Lee, CEO of Qeexo. “Our work with ST has now enabled application developers to quickly build and deploy machine-learning algorithms on ST’s MLC sensors without consuming MCU cycles and system resources, for an unlimited range of applications, including industrial and IoT use cases.” 

Adapting Qeexo AutoML for ST’s machine-learning core sensors makes it easier for developers to quickly add embedded machine learning to their very-low-power applications,” said Simone Ferri, MEMS Sensors Division Director, STMicroelectronics. “Putting MLC in our sensors, including the LSM6DSOX or ISM330DHCX, significantly reduces system data transfer volumes, offloads network processing, and potentially cuts system power consumption by orders of magnitude while delivering enhanced event detection, wake-up logic, and real-time Edge computing.” 

About Qeexo

Qeexo is the first company to automate end-to-end machine learning for embedded edge devices (Cortex M0-M4 class). Our one-click, fully-automated Qeexo AutoML platform allows customers to leverage sensor data to rapidly build machine learning solutions for highly constrained environments with applications in industrial, IoT, wearables, automotive, mobile, and more. Over 300 million devices worldwide are equipped with AI built on Qeexo AutoML. Delivering high performance, solutions built with Qeexo AutoML are optimized to have ultra-low latency, ultra-low power consumption, and an incredibly small memory footprint. For more information, go to www.qeexo.com.

About STMicroelectronics

At ST, we are 46,000 creators and makers of semiconductor technologies mastering the semiconductor supply chain with state-of-the-art manufacturing facilities. An independent device manufacturer, we work with more than 100,000 customers and thousands of partners to design and build products, solutions, and ecosystems that address their challenges and opportunities, and the need to support a more sustainable world. Our technologies enable smarter mobility, more efficient power and energy management, and the wide-scale deployment of the Internet of Things and 5G technology. Further information can be found at www.st.com.

For Press Information Contact:

Lisa Langsdorf
GoodEye PR for Qeexo
Tel: +1 347 645 0484
Email: [email protected]

Michael Markowitz
Director Technical Media Relations
STMicroelectronics
Tel: +1 781 591 0354
Email: [email protected]

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Lose the MCU: ST and Qeexo Simplify Machine Learning with AutoML

All About Circuits 19 May 2021

Intelligent decision-making has moved to the edge with machine learning, though deployment could be complicated. Qeexo and STMicroelectronics are looking to change that.

Read the full article here: https://www.allaboutcircuits.com/news/lose-the-mcu-st-and-qeexo-simplify-machine-learning-with-automl/

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Qeexo Collaborates with ST to Automate Machine Learning on Machine Learning Core (MLC) Sensors

Qeexo 18 May 2021

Company also debuts the Qeexo Model Converter to optimize customers’ existing machine learning models for the Edge


MOUNTAIN VIEW, CALIF. (PRWEB) MAY 18, 2021

Qeexo, developer of an automated machine learning (ML) platform that accelerates the development of tinyML models for the edge, today announced that it is working with STMicroelectronics to enable developers to create machine learning models for ST’s Machine Learning Core (MLC) sensors, so that inferences can run right on the sensor, without the need for a microcontroller. This feature will be available to users in Q2.

Traditionally, due to limited computation power, memory size, and battery life, building machine learning solutions for edge devices had been challenging. Qeexo AutoML solves this. Its one-click, fully automated platform allows customers to rapidly build machine learning solutions for edge devices using sensor data. By moving machine learning to embedded processors and now sensors on edge devices, developers can improve privacy, latency, and availability.

“Qeexo continues to demonstrate technical leadership in the embedded machine learning space by automating machine learning on tiny, resource-constrained devices – this time, on a Machine Learning Core sensor, independent from an MCU,” said Sang Won Lee, CEO of Qeexo. “For use cases that can benefit from machine learning, but do not have access to MCUs due to cost, power, latency, or infrastructure constraints, there are significant advantages to running machine learning on the sensor, including cost and power savings.”

“Many IoT solutions developers are looking to easily add embedded machine learning to their very low power applications and need help to bridge the gap from concept to prototype to production,” said Simone Ferri, MEMS Sensors Division Director, STMicroelectronics. “We put MLC in our sensors to reduce system data transfer volumes and offload network processing. Qeexo AutoML can help unlock the benefits of inherently low-power sensor design, advanced AI event detection, wake-up logic, and real-time Edge computing.”

Qeexo also announced that it is launching a model converter that can take machine learning models in the ONNX format to optimize them for embedded devices. For customers who already have a machine learning team, and who have worked on and have existing machine learning models, they can use the Qeexo Model Converter to make them smaller and more optimized for embedded devices. The technology will also make it easier for developers who want to compare the performance of their hand-built models against the models automatically created with Qeexo AutoML.

In addition, Qeexo is now offering a machine learning consulting service to help clients jump-start their projects. Qeexo will first work with clients to develop and deploy commercial-ready machine learning solutions, then tailor Qeexo AutoML to fit customer needs. Qeexo will provide the knowledge transfer necessary for client teams to continue to use Qeexo AutoML for current and future projects.

About Qeexo
Qeexo is the first company to automate end-to-end machine learning for embedded edge devices (Cortex M0-M4 class). Our one-click, fully-automated Qeexo AutoML platform allows customers to leverage sensor data to rapidly build machine learning solutions for highly constrained environments with applications in industrial, IoT, wearables, automotive, mobile, and more. Over 300 million devices worldwide are equipped with AI built on Qeexo AutoML. Delivering high performance, solutions built with Qeexo AutoML are optimized to have ultra-low latency, ultra-low power consumption, and an incredibly small memory footprint.

For more information, go to www.qeexo.com.

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Building effective IoT applications with tinyML and automated machine learning

Embedded 27 January 2021

IoT enables continuous monitoring of environments and machines using tiny sensors. Advances in sensor technologies, microcontrollers, and communication protocols made mass production of IoT platforms, with many connectivity options, possible at affordable prices. Due to the low cost of IoT hardware, sensors are being deployed on a large scale at public places, residentials, and on machines.

Read the full article here: https://www.embedded.com/building-effective-iot-applications-with-tinyml-and-automated-machine-learning/

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The insideBIGDATA IMPACT 50 List for Q1 2021

insideBIGDATA 05 January 2021

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they’re impacting the enterprise through leading edge products and services. We’re happy to publish this evolving list of the industry’s most impactful companies!

Read the full article here: https://insidebigdata.com/2021/01/05/the-insidebigdata-impact-50-list-for-q1-2021/