- 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
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.
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