Renesas Electronics Corporation has completed the acquisition of Greece-based AI software company Irida Labs, strengthening its embedded processing and edge AI software capabilities for machine vision and intelligent camera applications.
The acquisition is aimed at expanding Renesas’ offerings in edge-based AI systems, particularly in applications such as industrial automation, robotics, smart cities, healthcare, agriculture and IoT deployments. The company said Irida Labs’ software and development tools will also be integrated into Renesas 365, its recently launched cloud-based electronics system development platform.
According to the company, the move comes as demand grows for AI-enabled systems that process visual data locally on devices rather than relying on cloud-based computing. Renesas said developers continue to face challenges related to power-efficient processing, AI model deployment, latency and data security in edge AI environments.
Irida Labs develops embedded software for AI-powered visual perception systems, including its PerCV.ai platform. Renesas said combining this software stack with its RA microcontrollers and RZ microprocessors would allow it to offer more integrated Vision AI systems with lower power consumption and reduced development complexity.
Gaurang Shah, Vice President and General Manager of Renesas’ Embedded Processing Product Group, said, “With Irida Labs’ Vision AI tools, software and highly competent AI engineers now part of Renesas, our solution brings together AI perception, embedded processing, development tools and system integration to significantly reduce the learning curve for developers.”
Vassilis Tsagaris, CEO and Co-Founder of Irida Labs, added that the acquisition would combine the company’s edge Vision AI expertise with Renesas’ hardware portfolio and ecosystem to support wider deployment of edge AI systems globally.
Renesas said bringing the technology in-house would enable faster delivery of integrated solutions and expand the capabilities of the Renesas 365 platform over time.