New Integration Enables MATLAB and Simulink Workflows on Renesas RA and RH850 Microcontrollers, Streamlining Code Generation, Deployment, and On-Hardware Execution for Faster Validation and Iteration

The RZ/G2L microprocessor includes a Cortex®️-A55 (1.2GHz) CPU, 16-bit DDR3L/DDR4 interface, 3D graphics engine with Arm®️ Mali-G31 and video codec (H.264). It also has many interfaces such as camera input, display output, USB 2.0, and Gbit-Ether, making it ideal for applications such as entry-class industrial human-machine interfaces (HMIs) and embedded devices with video capabilities.

If you listen to the mainstream AI conversation, you might assume that every meaningful deployment requires racks of GPUs, enormous training datasets, and a Cloud energy budget that could power a small city. That perception comes largely from the enterprise AI world. Large language models, enterprise analytics platforms, and recommendation engines dominate the headlines.

Rich Nass, contributing Editor with Embedded Computing Design talks with Leigh Gawne, Vice President, Head of R&D at Renesas 365 at embedded world 2026.

At this year's embedded world exhibition and conference, Renesas announced the general availability of its Renesas 365, an open end-to-end electronics development platform powered by Altium.

Physical AI is a term that’s in vogue right now. It refers to the application of AI to systems that directly interact with and influence the physical world. Unlike Cloud-based AI models that analyze data and produce insights, physical AI closes the loop between perception, decision-making, and actuation. It senses its environment, processes that data locally or at the Edge, and then potentially drives such equipment as motors, valves, pumps, or actuators. In short, it turns algorithms into action.

For decades, embedded development has followed a fairly predictable path.