MUNICH, Germany and TOKYO, Japan ― Renesas Electronics Corporation launched the new generation of automotive fusion system-on-chips (SoCs), serving multiple automotive domains including Advanced Driver Assistance Systems (ADAS), in-vehicle infotainment (IVI), and gateway applications on a single chip.

San Jose, California. Tessolve and Renesas collaborated to develop Tessolve’s SMARC SOM compatible with SMARC standard 2.1 and based on the RZ/V2H MPU leveraging four Arm Cortex-A55 CPU cores alongside two Cortex-R8 cores, one Cortex-M33, and an AI Accelerator (DRP-AI3) capable of eight TOPS/dense and 80 TOPS/sparse.

In a rare move, I had two guests join me for this week’s Embedded Executives podcast, but for a good reason. I was joined by Mohammed Dogar, Vice President and Head of Global Business Development and Ecosystem at Renesas Electronics, and Sakya Dasgupta, Founder and CEO of EdgeCortix.

TOKYO. Renesas Electronics recently introduced its RA8 Series of microcontrollers (MCUs), which provide a performance level of over 3000 CoreMark points, as well as deterministic, low latency, real-time operation. The series' implementation of the Arm Cortex-M85 processor enables 6.39 CoreMark/MHz performance, which will allow system designers to use the RA MCUs in applications that previously required microprocessors (MPUs). The new series is part of Renesas’ popular RA Family of MCUs based on Arm Cortex processors. Existing designs built for other RA devices can easily be ported to the new RA8 MCUs.

When it comes to the ecosystem, particularly around AI, you need all the parts to be available if you want to develop a system. We know that ecosystems forever continue to evolve, but it’s safe to dive into the waters at this point, according to Mohammed Dogar, the Vice President and Head of Global Business Development and Ecosystem at Renesas Electronics.  In this week’s Embedded Executive podcast, we discuss, among other things, how the AI ecosystem has evolved, and what users can expect at this point.

Are you wondering what would be considered a practical application for AI at the endpoint (the place where the data is captured)? Well, you’re in luck, because Rich is moderating a panel at Renesas’ AI Live virtual conference that specifically covers that topic. 
 

When it comes to AIoT, some of the terms and techniques you’ll need to familiarize yourself with include Edge computing, machine learning (ML), TinyML, anomaly detection, natural language processing, computer vision, and predictive maintenance.