Infineon Technologies AG last week acquired Stockholm-based Imagimob AB, one of the most active players bringing AI to edge devices.
[Published May 24, 2023] – Germany-based chip maker Infineon Technologies AG last week acquired Stockholm-based Imagimob AB, one of the most active players among a slew of startups seeking to bring AI-based machine learning to embedded devices on the edge of the Internet of Things (IoT). Terms were not disclosed.
Imagimob provides end-to-end development tools and cloud-based services intended to bring the much-vaunted capabilities of neural machine learning (ML) models from the cloud data center to edge devices. These devices have small footprints, rigorous memory limits, and strict constraints on power consumption. The aspiration to do a lot with little is summed up in the umbrella term “TinyML.”
The edge AI devices that Imagimob seeks to support also must cope with a wide variety of sensor types, including sensors that measure and analyze vision, movement, pressure, heat, velocity and other data formats. Business uses are broad, ranging from surveillance cameras and refrigerator monitors in retail settings to actuators and anomaly detectors in oil industry field equipment, and beyond.
Infineon’s Thomas Rostech said the purchase is based on his company’s contention that artificial Intelligence and machine learning are about to enter every embedded application, enabling new functionalities. In a statement, Rosteck, who is president of Infineon’s Connected Secure Systems division, boosted Imagimob’s platform and expertise in developing machine learning for edge devices.
In recent years, Infineon has worked to build out a portfolio of advanced sensors and IoT solutions. This is an area in which software is expected to play a key role. For example, the market for edge AI software is set to grow to €10.0B in 2032, from €738.5M in 2022, for a CAGR of 29.8% over the forecast period, according to Global Edge AI Software Market Research.
Founded in 2013, Imagimob tech- and business-side leaders came out of the mobile applications market. Since inception, its teams have worked on a wide variety of edge AI use cases. These include gunshot and other audio event detection, fall detection, condition monitoring, signal classifiers, safety vests, and more.
Imagimob has been highly active within the TinyML community, centered in part around the TinyML Foundation, which is dedicated to nurturing ultra-low power machine learning. Imagimob software has been demoed in showcases with Synaptics, Syntiant, Texas Instruments, and other edge AI hardware concerns.
Responding to request for comment, an Infineon spokesperson said the company plans to integrate Imagimob into its organizational structure and that customer relationships with Imagimob’s customers will continue, including partners working on competitor’s hardware, “in alignment with the compliance regulations.”
To date, IoT growth has been fitful in the enterprise, as businesses look to move past proof-of-concept projects and achieve return-on-investment. Potential enterprise application areas that include retail, healthcare, supply chain and other operations are places where processing data on the edge translates into cost savings versus processing data in the cloud. The need to off-load processing to the edge becomes more acute as data intensive AI and machine learning capabilities come into play. Imagimob efforts to enable AI’s march from data center to the Internet’s edge are expected to fill out more fully with the backing of the larger chip maker Infineon.
Enterprise IoT has lost some luster in recent years as vendors grapple with a very extensive array of use cases. Intelligence in the form of machine learning makes sense, and so does the rise of TinyML as a next stage in delivering on the wide promise of IoT. But deep resources and breakthroughs on the software development side are required. That is at the same time that the venture capital markets have become less benign. So, more matches such as Infineon’s and Imagimob can be anticipated. – Jack Vaughan