STMicroelectronics Acquires AI Specialist Cartesiam
STMicroelectronics has reported a transaction with Cartesiam to acquire its assets (including its IP portfolio) and to transfer and integrate its employees. Closing is subject to regulatory approvals.
Cartesiam, based in Toulon (France), is a software company founded in 2016, which specializes in artificial intelligence (AI) development tools enabling machine-learning and inferencing on Arm-based microcontrollers, which today power billions of devices.
“Using artificial intelligence to create ever-smarter solutions is one of the priorities of our customers regardless of their size or industry,” said Claude Dardanne, President, Microcontrollers and Digital ICs Group, at STMicroelectronics. “With STM32Cube.AI, STMicroelectronics already offers the ability to map and run pre-trained artificial neural networks on our broad portfolio of STM32 microcontrollers. The addition of Cartesiam’s machine learning technology to STMicroelectronics’ existing solutions will provide the best edge-AI solution portfolio on the market for any customer looking to bring additional innovation to their offering.”
Its team includes data scientists and embedded signal processing experts, with significant experience in delivering standard and custom solutions. Its flagship and patented solution, NanoEdge AI Studio, allows embedded systems designers without prior knowledge in AI to rapidly develop specialized libraries integrating machine-learning algorithms directly into a broad range of applications.
Devices leveraging Cartesiam’s technology are already in production around the world included inside connected devices, household appliances, and industrial machines.
With this acquisition, STMicroelectronics reinforces its AI strategy and strengthens its technology portfolio to address the full spectrum of embedded machine-learning needs.
The NanoEdge AI Studio solution is fully complementary to STMicroelectronics’ STM32Cube.AI toolset and will provide STMicroelectronics’ customers with additional flexibility to integrate machine-learning into their solution.