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Qeexo AutoML Enables Machine Learning on Arm Cortex-M0 and Cortex-M0+

Qeexo, Co. 09 September 2020

First company to build an automated ML platform for the Arm Cortex-M0 and Cortex-M0+ processor

MOUNTAIN VIEW, Calif. (PRWEB) September 09, 2020

Qeexo, developer of an automated machine learning (ML) platform that accelerates the deployment of tinyML at the edge, today announces that its Qeexo AutoML platform now supports machine learning on Arm® Cortex®-M0 and Cortex®-M0+ processors, which power devices including sensors and microcontrollers from companies such as Arduino, Renesas, STMicroelectronics, and Bosch Sensortec.

The Arm Cortex-M0 processor is the smallest Arm processor available, and the Cortex-M0+ processor builds on Cortex-M0 while further reducing energy consumption and increasing performance. Qeexo is the first company to automate adding machine learning to a processor of this size. The Cortex-M0 and Cortex-M0+ processors are designed for smart and connected embedded applications, and are ideal for use in simple, cost-sensitive devices due to the lower power-consumption and ability to extend the battery life of critical use cases such as activity trackers.

Machine learning models built with Qeexo AutoML are highly optimized and have an incredibly small memory footprint. Models are designed to run locally on embedded devices, ideal for ultra low-power, low-latency applications on MCUs and other highly constrained platforms.

“This integration delivers the advantages of data processing at the edge to even the smallest of devices,” said Sang Won Lee, co-founder and CEO of Qeexo. “Qeexo AutoML, combined with the accessibility of MCUs from companies such as Arduino, Renesas, STMicroelectronics, and Bosch Sensortec, greatly benefits application developers, who can now build smart hardware products with relative ease.”

The growing list of machine learning algorithms supported on Qeexo AutoML currently include: GBM, XGBoost, Random Forest, Logistic Regression, Decision Tree, SVM, CNN, RNN, CRNN, ANN, Local Outlier Factor, and Isolation Forest. Several hardware platforms from Arduino, Renesas, and STMicroelectronics work with Qeexo AutoML out-of-the-box.

Supporting Partner Quotes

Arm

“Today even the smallest devices can contain some layer of artificial intelligence and machine learning. The Cortex-M0 and Cortex-M0+ processors pack high performance with very low power consumption, and the added support of the Qeexo AutoML platform enables application developers to easily add intelligence to small devices such as wearables, making a world of one trillion intelligent devices a closer reality.”

— Steve Roddy, Vice President of Product Marketing, Machine Learning Group of Arm

Arduino

“Arduino is on a mission to make machine learning simple enough for anyone to use. We’re excited to partner with Qeexo AutoML to accelerate professional embedded ML development by guiding users to the optimal algorithms for their application. Combined with Arduino Nano 33 IoT, users can quickly create smart IoT sensors that can perform analytics at the edge, minimize communication, and maximize battery life.”

– Dominic Pajak, VP Business Development, Arduino

Bosch

“Bosch Sensortec and Qeexo are collaborating on machine learning solutions for smart sensors and sensor nodes. We are glad that Qeexo’s AutoML has added support for Cortex-M0 families, to which Bosch Sensortec’s smart sensors like BMF055 belongs. We are excited to see more applications made possible by combining the smart sensors from Bosch Sensortec and AutoML from Qeexo.”

– Marcellino Gemelli, Director of Global Business Development at Bosch Sensortec

Renesas

“Renesas and Qeexo collaborated on the design of a new RA-Ready sensor board: the RA6M3 ML Sensor Module. Equipped with various motion and environmental sensors and enhanced with Qeexo AutoML, this sensor module is the perfect reference platform for developing intelligent machine learning applications.”

– Kaushal Vora, Director of Strategic Partnerships & Global Ecosystem at Renesas

STMicroelectronics

“Qeexo AutoML recently added support for our STWIN industrial platform, which features embedded industrial-grade sensors and an ultra-low-power microcontroller for vibration analysis. By automating the development of ML solutions for advanced industrial IoT applications such as condition monitoring and predictive maintenance, Qeexo AutoML eases the usability of our products.”

– Pierrick Autret, Product Marketing Engineer at STMicroelectronics

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About Qeexo
Qeexo is the first company to automate end-to-end machine learning for embedded edge devices (Cortex-M0-to-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, mobile, automotive, and more.

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. As billions of sensors collect data on every device imaginable, Qeexo can equip them with machine learning to discover knowledge, make predictions, and generate actionable insights.

Spun out of Carnegie Mellon University, Qeexo is venture-backed and headquartered in Mountain View, CA, with offices in Pittsburgh, Shanghai, and Beijing. To learn more, visit https://qeexo.com.

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Qeexo Takes Misery Out of EdgeML

Electronic Engineering Journal 14 July 2020

Startup Takes a Dose of its Own Medicine

Read the full article at: https://www.eejournal.com/article/qeexo-takes-misery-out-of-edgeml/

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Qeexo AutoML Now Hosted on AWS, Adds Algorithm Support

Embedded Computing 16 June 2020

The latest release of Qeexo AutoML makes the automated TinyML model development and deployment platform available as a web application hosted on Amazon Web Services (AWS).

Read the full article at: https://www.embedded-computing.com/machine-learning/qeexo-automl-now-hosted-on-aws-adds-algorithm-support#

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Qeexo Takes ‘TinyML’ to AWS Cloud

Enterprise AI

“Qeexo, the Carnegie Mellon University spinoff, is expanding public cloud access to its automated machine learning platform as it pushes its no-code “TinyML” approach to the network edge.”

Read the full article at: https://www.enterpriseai.news/2020/06/08/qeexo-takes-tinyml-to-aws-cloud/

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AutoML Mentioned in insideBIGDATA Latest News 6/12

InsideBIGDATA 15 June 2020

Qeexo Announces General Availability of the Qeexo AutoML Platform to Enable TinyML for Edge Devices

Read the full article at: https://insidebigdata.com/2020/06/12/insidebigdata-latest-news-6-11-2020/

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Qeexo Announces General Availability of the Qeexo AutoML Platform to Enable TinyML for Edge Devices

Qeexo, Co. 08 June 2020

MOUNTAIN VIEW, CA (PRWEB) JUNE 08, 2020

Qeexo, developer of an automated machine learning (ML) platform that accelerates the deployment of TinyML at the edge/endpoint, announced today the general availability of its Qeexo AutoML platform on Amazon Web Services (AWS).

“We are excited to announce the general availability of Qeexo AutoML as a web application hosted on AWS. With an intuitive end-to-end workflow and easy online access, Qeexo AutoML will significantly improve the efficiency of TinyML model development and deployment for all users from novices to expert data scientists,” said Sang Won Lee, CEO of Qeexo.

Beginning today, users can sign up at https://automl.qeexo.com, for a “Bronze” package where they can upload or collect datasets and automatically build lightweight machine learning models that can be deployed to, and tested on, select embedded hardware platforms. The Bronze evaluation package is FREE for a limited time.

“Qeexo AutoML now has advanced control features, new machine learning algorithms, and several new hardware platform support that will provide more flexibility for the TinyML developers,” added Lee.

New key features include: manual selection of sensors post data recording and sensor data features in model building; class-separability visualizations; fine-tuning of classification sensitivity using visualization and sensitivity analysis; and configuration for neural network parameters including quantization-aware training. These new features enable users to build predictive maintenance solutions to detect anomalies in industrial machines; gesture and context awareness algorithms for consumer/wearable use cases such as fitness trackers and elderly care; and other machine-learning-based algorithms for sensor-enabled smart IoT devices.

Significant model updates are also being released, including: a classifier ideally suited for anomaly detection in industrial applications and support of Recurrent Neural Network (RNN), Isolation Forest, and Local Outlier Factor algorithms. This adds to the existing extensive algorithm support of ANN, CNN, GBM, XGBoost, Random Forest, Logistic Regression, and Decision Tree. Qeexo AutoML enables sensor data collection and visualization, automated model building, and one-click deployment on the following hardware platforms: Arduino Nano 33 BLE Sense, Renesas RA6M3 ML Sensor Module, STMicroelectronics STWINKT1, and STMicroelectronics SensorTile.box.

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 mobile, IoT, wearables, automotive, and more.
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.
As billions of sensors collect data on every device imaginable, Qeexo can equip them with machine learning to discover knowledge, make predictions, and generate actionable insights.
Spun out of Carnegie Mellon University, Qeexo is venture-backed and headquartered in Mountain View, CA, with offices in Pittsburgh, Shanghai, and Beijing. To learn more, visit http://automl.qeexo.com.

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Qeexo named in insideBIGDATA IMPACT 50 List for Q2 2020

InsideBIGDATA 17 April 2020

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 at: https://insidebigdata.com/2020/04/09/the-insidebigdata-impact-50-list-for-q2-2020/

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Qeexo is making machine learning accessible to all

Stacey on IOT 20 March 2020

Every now and then I see technology that’s so impressive, I can’t wait to write about it, even if no one else finds it cool. I had that experience last week while watching a demonstration of a machine learning platform built by Qeexo.

Read the full article at: https://staceyoniot.com/qeexo-is-making-machine-learning-accessible-to-all/

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Qeexo AutoML is Now Live on Amazon Web Services

Qeexo, Co. 27 February 2020

Qeexo also announces new features and support for the Renesas RA Family of 32-bit MCUs

MOUNTAIN VIEW, CALIF. (PRWEB) FEBRUARY 27, 2020 Qeexo today announces several new features for its AutoML product, including support for Amazon Web Services (AWS) and tools designed to give Qeexo AutoML users greater flexibility and performance in the collection and analysis of sensor data:

  • Support for AWS: Users of Qeexo’s AutoML now have the option to install the product locally on their private server, or to access it via AWS. This allows for increased scalability to serve more users and build more complex models faster by leveraging the Cloud.
  • More machine learning models: Users can now select more machine learning algorithms (both deep-learning and non-deep-learning) when building models.
  • Data and model visualization: Users can visualize the collected/uploaded sensor data, and also see more details for each model, including various graphs and charts that increases explainability.
  • Support for microphone sensors: Users can now collect and analyze data from a device’s microphone, greatly augmenting the supported use cases (e.g. keyword spotting).

Qeexo also announces that its Qeexo AutoML product now supports the Renesas RA Family of Cortex-M MCUs. Qeexo AutoML is a fully-automated, end-to-end platform that builds lightweight machine learning solutions at the Edge. The Renesas RA Family of 32-bit MCUs is designed to help device developers create next-generation secure and low-power IoT devices. The combination will enable developers to rapidly build machine learning solutions for Edge devices using Renesas hardware and Flexible Software Package (FSP).

International Data Corporation (IDC) estimates that there will be 41.6 billion connected IoT devices, or “things,” generating 79.4 zettabytes (ZB) of data in 2025.

“As the number of IoT devices grows, companies increasingly struggle to make sense of the vast amount of data that they generate,” said Sang Won Lee, CEO of Qeexo. “With Qeexo AutoML, we allow even non-experts to rapidly build machine learning models that run at the Edge, in order to analyze this data and generate actionable insights in real-time.”

“Integrating our RA MCUs with an automated machine learning tool like Qeexo AutoML will greatly augment the user experience and applicability of our products. It will also make it easier for our joint customers to navigate the process from prototyping to production without prior expertise or much experience with artificial intelligence and machine learning,” said Kaushal Vora, Director of Strategic Partnerships & Global Ecosystem at Renesas Electronics Corporation. “We will continue our collaboration with Qeexo to enable even more Renesas hardware on Qeexo AutoML.”

Qeexo AutoML greatly simplifies the development process for machine learning solutions, allowing companies to create machine learning models without having to invest in expensive, in-house machine learning teams, resulting in huge time and cost savings. Qeexo AutoML can automatically generate models that can run locally on environments constrained by power and memory, such as IoT devices, wearables, and automotive sensors.

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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 mobile, IoT, wearables, automotive, and more.
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.

As billions of sensors collect data on every device imaginable, Qeexo can equip them with machine learning to discover knowledge, make predictions, and generate actionable insights.
Spun out of Carnegie Mellon University, Qeexo is venture-backed and headquartered in Mountain View, CA, with offices in Pittsburgh, Shanghai, and Beijing. To learn more, visit https://automl.qeexo.com.

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Machine Learning on the Edge, Hold the Code

Datanami

Many companies are scrambling to find machine learning engineers who can build smart applications that run on edge devices, like mobile phones. One company that’s attacking the problem in a broad way is Qeexo, which sells an AutoML platform for building and deploying ML applications to microcontrollers without writing a line of code.

Read the full article at: https://www.datanami.com/2020/02/25/machine-learning-on-the-edge-hold-the-code/