Category: FEATURED

TDK announces availability of automated ML Platform Integration for Arm® Keil® MDK

TDK 14 March 2023

TDK’s new group company Qeexo launches AutoML for Arm Keil MDK
Qeexo AutoML enables end-to-end embedded machine learning and development workflows with QeexoAutoML and Arm Keil MDK
Qeexo AutoML automatically builds machine learning solutions optimized for ARM processors

March 14, 2023

TDK Corporation (TSE 6762) announced the availability of the first automated ML platform integration for Arm Keil MDK from Qeexo, a TDK group company. The Qeexo AutoML platform supports a wide range of machine learning algorithms and is designed for lightweight Cortex-M0 to -M4 class processors with ultra-low latency and power consumption. The platform allows customers to leverage sensor data to rapidly build and deploy machine learning solutions. The Qeexo AutoML platform requires an incredibly small memory footprint, making it optimal for applications in industrial, IoT, wearables, automotive, mobile, and other highly constrained environments.

Qeexo AutoML’s integration for Arm Keil MDK supports seamless, streamlined, end-to-end embedded machine learning development workflows, enabling integration of output libraries from Qeexo AutoML. The integration encapsulates the ML model into the Arm Keil IDE using the CMSIS-Pack mechanism for running the final custom binary application on an Arm Cortex based MCU. Qeexo AutoML provides a no-code environment, enabling data collection and training of different machine learning algorithms, including both neural networks and non-neural-networks, to the same dataset. It generates metrics for each (accuracy, memory size and latency), so that users can pick the model that best fits their unique requirements. Qeexo AutoML streamlines intuitive process automation, enabling customers without precious ML resources to design Edge AI capabilities for their own specific applications.

“As machine learning (ML) becomes increasingly prevalent in embedded and IoT, it’s critical that we empower embedded software developers to navigate this new area and continue to innovate,” said Reinhard Keil, senior director, embedded technology, Arm. “By abstracting the entire ML development process with a powerful and easy-to-use graphical user interface, Qeexo AutoML enables rapid build, test, and deployment of ML models to Arm Keil MDK allowing embedded and IoT developers to harness the power of ML as they build new solutions on Arm.”

Sang Lee, CEO, Qeexo noted, “Qeexo AutoML’s integration with Arm Keil MDK closes the gap between machine learning and embedded development, enabling effortless integration of Qeexo AutoML models to any Arm Keil MDK project.”

Qeexo AutoML with Arm Keil MDK support will be available at the end of Q1 2023.

TDK will present magnetic solutions, sensors, and embedded motor control solutions as well as power supply solutions, components, and software for Internet-of-Things applications at Embedded World 2023 exhibition and conference, March 14-16, 2023, at the Nürnberg Messe, Nürnberg, Germany, and can be found at Hall 1 – #1- 550. Qeexo will demonstrate their machine learning platform solution within the TDK booth and showcase their full range of technology solutions within the Arm pavilion Hall 4 – #4-504. For additional information on the Qeexo ML platform, please visit https://qeexo.tdk.com or contact Qeexo Sales at https://qeexo.tdk.com/contact-us/.

Glossary

AutoML: Automated machine learning is the process of automating tasks of applying machine learning to real-world problems.
tinyML: Tiny machine learning is broadly defined as a fast-growing field of machine learning technologies that can perform on-device sensor data analytics at extremely low power.
ML: Machine learning is a field of inquiry devoted to understanding and building methods that ‘learn’, that is, methods that leverage data to improve performance on some set of tasks
Smart Edge solutions: Smart Edge solutions refers to the analysis of data and development of solutions at the site where the data is generated.
Smart Edge device: An intelligent edge device is a sophisticated IoT device that performs some degree of data processing within the device itself.
Arm Keil MDK: Is a comprehensive software development solution for Arm®-based microcontrollers and includes all components that you need to create, build, and debug embedded applications.

Main applications

Industrial IoT for manufacturing
Automotive
Medical
Leisure, sports, and fitness activity monitoring for wearable sensors
Indoor/outdoor navigation (dead-reckoning, floor/elevator/step detection)
Smart home appliances such as robotic vacuum cleaners
Condition based monitoring, predictive maintenance
Machine learning platform and applications development
Developer tools, IDE

Main features and benefits

Fully automated, no code machine learning platform
Seamless integration of Qeexo models with Arm Keil MDK
Supports 17 different machine learning algorithms
Wide range of hardware support for Arm Virtual Hardware M-55 and U-55

About TDK Corporation

TDK Corporation is a world leader in electronic solutions for the smart society based in Tokyo, Japan. Built on a foundation of material sciences mastery, TDK welcomes societal transformation by resolutely remaining at the forefront of technological evolution and deliberately “Attracting Tomorrow.” It was established in 1935 to commercialize ferrite, a key material in electronic and magnetic products. TDK’s comprehensive, innovation- driven portfolio features passive components such as ceramic, aluminum electrolytic and film capacitors, as well as magnetics, high-frequency, and piezo and protection devices. The product spectrum also includes sensors and sensor systems such as temperature and pressure, magnetic, and MEMS sensors. In addition, TDK provides power supplies and energy devices, magnetic heads and more. These products are marketed under the product brands TDK, EPCOS, InvenSense, Micronas, TDK Qeexo, Tronics and TDK-Lambda. TDK focuses on demanding markets in automotive, industrial and consumer electronics, and information and communication technology. The company has a network of design and manufacturing locations and sales offices in Asia, Europe, and in North and South America. In fiscal 2022, TDK posted total sales of USD 15.6 billion and employed about 117,000 people worldwide.

About Qeexo

Qeexo, a TDK Group Company, is the first company to automate end-to-end machine learning for embedded edge devices (Cortex M0-M4 class). Its 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. https://qeexo.tdk.com

Images related to this release can be downloaded from the following URL: https://www.tdk.com/en/news_center/press/20230314_02.html

Further information on the products can be found under https://qeexo.tdk.com

Contacts for regional media

Global
Mr. David A. ALMOSLINO
TDK USA Corporation, San Jose, CA
+1-408-501-2278
david.almoslino@tdk.com

North America
Ms. Sarah MACKENZIE
Publitek, Portland, OR
+1 503-720-3743
TDK-global@publitek.com

Japan
Mr. Yoichi OSUGA
TDK Corporation, Tokyo, Japan
+813-6778-1055
pr@jp.tdk.com

Worldwide
Mr. Sang Won Lee
Qeexo, Mountain View, CA
+1 510-508-0446
sang.lee@tdk.com

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Qeexo AutoML 1.19.0 New Feature Introduction

Cora Zhang 18 January 2023

In this article, we are going to introduce you some of the latest and greatest features and improvements released in Qeexo AutoML 1.19.0.

Feature 1 – ‘FILTER ENVIRONMENT’ feature, DATA page

Image 1 – Where to find the Filter Environment feature

‘FILTER ENVIRONMENT’ enables you to filter your data collections with different environments settings and sensor configurations settings.

When the user clicks the ‘FILTER ENVIRONMENT’ button, there will be a pop-up window. On the very top area of the window, there is a drop-down menu for users to select to list either all environments or any specific environment. Moving down, there is a two columns table where users will see environment names and sensor configurations settings. Users can click or unclick the checkbox to the left to select any environment(s), then click FILTER button to apply. Once option has been applied, users will be navigated back to DATA page, and only those data collections that match with the selection will be displayed under the DATA page.

Video 1 – Filter Environment

Feature 2 – ‘SELECT ALL’ feature under DATA page

Image 3 – The new data page SELECT ALL feature

‘SELECT ALL’ button enables users to select all data collections listed under DATA page with a single click. This feature is very helpful when users have many data collections to select to train a model. It saves users a lot of time and work. If users have data collections that are sharing different environments or different sensor configurations, users could use ‘FILTER ENVIRONMENT’ button to filter data collections, only keep those that need to be used. Then use the ‘SELECT ALL’ checkbox.

Please note that although users are allowed to click to select all data collections regardless of sensor configuration settings, the START NEW TRAINING button will only become clickable when all selected data collections share the same sensor configuration. If there’s multiple pages of data collections, users will need to SELECT ALL for each page.

Video 2 – Select All

Feature 3 – Upgraded ‘Data Segmentation’ feature under DATA page

Image 4 – The Create New Segment pop-up window

The upgraded Data Segmentation features save users time and work on cropping a data collection into many different segments.

User can click on ‘CREATE NEW SEGMENT’ button to create a segment label, then select a color. After users click the ‘CREATE’ button, users will be able to click the start and end point of a segment on the data plot as many times as needed. If there’s another segment label users would like to create, users could simply repeat the process to crop data with a new segment label.

Video 3 – Create new segment

Every time a user creates a segment, the segment will automatically be displayed in the segments list under the data plot. Users have the option to edit (including name, duration (start and end time)) or delete it.

Video 4 – Edit segment

To help users match the segments on the plot with the lines under plot, we have added a click and reveal feature to indicate the corresponding segments from the plot when the user clicks the segment’s checkbox.

Image 7 – Selecting a segment automatically highlights it in the graph

Video 5 – Click and reveal segment

Additionally, by clicking the check box to the left of the segment label(s) on the top, it will select all segments. Users could also click the trash button on top right to delete all selected segments at once.

Video 6 – Select and delete all segments

Feature 4 – ASSISTED SEGMENTATION feature under DATA page

Image 10 – The Assisted Segmentation pop-up window

Assisted Segmentation provides users with an even quicker way to crop a data collection into segments. Users just need to manually crop a few segments for each label and then use Assisted Segmentation to help detect those data points that share the same information. More specifically, users could follow the steps in Feature 3 – click ‘CREATE NEW SEGMENT’, create a segment label, click the start and end point on data plot to crop a segment. Then users could simply click ‘ASSISTED SEGMENTATION’ button on the top right. There will be a pop-up window for users to select segment label(s) for assisted segmentation. Once users clicks NEXT, Qeexo AutoML will detect the segments for the un-cropped areas and list them on both the data plot and segments list below. Users could either ACCEPT or REJECT the result depending on how satisfied you are with the segmentation.

Image 11 – Assisted Segmentation results

Feature 5 – Advanced option of setting up ML Static Library Memory Constraint

When Pro Tier users select data collections and continue to click START NEW TRAINING button to start the model training, we added a new advanced option to allow users to configure ML Static Library Memory Constraint, more specifically, users could set the target ML Static Library maximum flash and RAM memory size usage as desired. Generally, the model performance will reduce as the model size reduces, but not by a lot. The tradeoff should be worth it.

In the ‘Algorithm Selection’ window, user could click CONFIGURE button to access to the Memory Constraint feature. The toggle on the right side should be enabled before users type in any values in the field on the left side.

Image 12 – Advanced options step 1 – Configure

Image 13 – Advanced options step 2 – Enable with the slider on the right, then enter settings

Feature 6 – Report on size taken by ML Model and Other code separately per ML library built under Model page

After users trained a model, under performance summary for each ML library (algorithm) built, we added a new feature to report the size occupied by ML model. From here, users could check the flash and RAM size of binary image, ML model, pre-processing + featurization and the total size. The very basic benefit is that it brings more model information for users to use as a reference when selecting the final model. We are sure it also provides more flexibility and possibilities per user case.

Feature 7 – DATA Augmentation Toolkit

Data augmentation is a new feature that we just added for Qeexo AutoML 1.19.0. The major function is to add more data to the training data which could help the model become more robust. This additional data is basically a fictious data which has been created by applying some operations to the original data. We currently support two types of augmentation – scaling and jittering. These two augmentations are enabled for all sensors except environmental sensors.

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TDK to acquire Qeexo to enable complete smart edge platforms

Qeexo 04 January 2023

TDK to acquire Qeexo, Co, a leading developer of automated machine-learning (ML) platform that accelerates the development of tinyML models for low power, always-on intelligent platforms
TDK aims to further strengthen its ML expertise and simplify ML application development to become a leader in delivering smart edge solutions
Acquisition enables TDK to accelerate the transition to Industry 4.0 with smart edge solutions

SAN JOSE, Calif., Jan. 4, 2023 /PRNewswire/ — TDK Corporation (TSE: 6762) (CEO & President: Noboru Saito, hereinafter “TDK”) announced that today TDK has agreed to acquire Qeexo, Co. (CEO: Sang Won Lee, hereinafter “Qeexo”), a U.S.-based venture-backed company spun out of Carnegie Mellon University engaged in the automation of end-to-end machine learning for edge devices. As a result of the acquisition, Qeexo will become a wholly owned subsidiary of TDK, subject to customary closing conditions, including approval of the Committee on Foreign Investment in the US (CFIUS).

Qeexo, based in Mountain View, California, USA, is the first company to automate end-to-end machine learning for edge devices. Qeexo AutoML enables a no-code environment, enabling data collection and training of 18 (and expanding) different machine learning algorithms, including both neural networks and non-neural-networks, to the same dataset, while generating metrics for each (accuracy, memory size, latency), so that users can pick the model that best fits their unique requirements. A cloud-based easy to use solution, it provides an intuitive UI platform system that allows users to collect, annotate, cleanse, and visualize sensor data and automatically build “tinyML” models using different algorithms. Qeexo’s AutoML platform allows customers to leverage sensor data to rapidly build machine learning solutions optimized to have ultra-low latency and power consumption, with an incredibly small memory footprint for highly constrained environments with applications in industrial, IoT, wearables, automotive, mobile, and more. Through streamlined intuitive process automation, Qeexo’s AutoML enables customers without precious ML resources and greatly accelerates design of Edge AI capabilities for their own specific applications.

“Qeexo brings together a unique combination of expertise in automating machine learning application development and deployment for those without ML expertise, high volume shipment of ML applications and understanding of sensors to accelerate the deployment of smart edge solutions,” stated Jim Tran, CEO, TDK USA Corporation. “Their expertise combined with TDK’s leadership positions in sensors, batteries and other critical components will enable the creation of system level solutions addressing a broad range of applications and industries.”

“Our platform is an outgrowth of our own history of high-volume ML application development and deployment enabling those with domain expertise but not ML expertise to solve real world problems quickly and efficiently,” continued Sang Lee, CEO, Qeexo. “We see our AutoML tool as a natural partner to the smarter sensor systems that TDK is building.”

The following is an outline of the company profile:

Company name: Qeexo, Co.
Location: Headquartered in Mountain View, CA, office in Pittsburgh, PA, USA
Established: September 2012
Management: CEO – Sang Won Lee; CTO – Chris Harrison
Main business operations: Development of automated machine-learning (ML) platform that accelerates the development of tinyML models for the Edge.
Learn more about fundamental machine learning concepts: Qeexo AutoML Best Practice Guide – Qeexo, Co.

TDK will be showcasing over 30 different technologies, solutions, and platforms at CES 2023, January 5-8, 2023, at the Las Vegas Convention Center (LVCC) and can be found at Central Hall – #16181. Qeexo will demonstrate their machine learning platform solution within the TDK booth and also showcase their full range of technology solutions at the Qeexo booth #11222, North Hall.

Glossary

AutoML: Automated machine learning is the process of automating the tasks of applying machine learning to real-world problems.
tinyML: Tiny machine learning is broadly defined as a fast-growing field of machine learning technologies that is capable of performing on-device sensor data analytics at extremely low power,
ML: Machine learning is a field of inquiry devoted to understanding and building methods that ‘learn’, that is, methods that leverage data to improve performance on some set of tasks
Smart Edge solutions: Smart Edge solutions refers to the analysis of data and development of solutions at the site where the data is generated.
Smart Edge device: An intelligent edge device is a sophisticated IoT device that performs some degree of data processing within the device itself.

About TDK Corporation
TDK Corporation is a world leader in electronic solutions for the smart society based in Tokyo, Japan. Built on a foundation of material sciences mastery, TDK welcomes societal transformation by resolutely remaining at the forefront of technological evolution and deliberately “Attracting Tomorrow.” It was established in 1935 to commercialize ferrite, a key material in electronic and magnetic products. TDK’s comprehensive, innovation-driven portfolio features passive components such as ceramic, aluminum electrolytic and film capacitors, as well as magnetics, high-frequency, and piezo and protection devices. The product spectrum also includes sensors and sensor systems such as temperature and pressure, magnetic, and MEMS sensors. In addition, TDK provides power supplies and energy devices, magnetic heads and more. These products are marketed under the product brands TDK, EPCOS, InvenSense, Micronas, Tronics and TDK-Lambda. TDK focuses on demanding markets in automotive, industrial and consumer electronics, and information and communication technology. The company has a network of design and manufacturing locations and sales offices in Asia, Europe, and in North and South America. In fiscal 2022, TDK posted total sales of USD 15.6 billion and employed about 117,000 people worldwide.

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.

Images related to this release can be downloaded from the following URL: https://www.tdk.com/en/news_center/press/20230104_01.html

Contacts for regional media

Region	Contact		Phone	Mail
Global	Mr. David A.ALMOSLINO	TDK USA Corporation San Jose, CA	+1 408-501-2278	david.almoslino@tdk.com
North America	Ms. Sarah MACKENZIE	Publitek Portland, OR	+1 503-720-3743	TDK-global@publitek.com
Japan	Mr. Yoichi OSUGA	TDK Corporation Tokyo, Japan	+813 6778-1055	pr@jp.tdk.com
Worldwide	Mr. Sang Won Lee	Qeexo Mountain View, CA	+1 510 508 0446	sang@qeexo.com

SOURCE TDK Corporation