Renesas unveil RZ/A2M Microprocessor for high-speed processing

RZ/A2M Microprocessor

Renesas Electronics Corporation is expanding its range of embedded artificial intelligence (e-AI) solutions for incorporating AI into embedded systems by bringing intelligence to endpoints.

Approximately 150 companies in more than 10 countries worldwide are already conducting trials based on this technology, and there are more than 30 actual e-AI use cases underway. Renesas has now developed a new RZ/A2M microprocessor (MPU) to expand the use of e-AI solutions to high-end applications. The new MPU delivers 10 times the image processing performance of its predecessor, the RZ/A1, and incorporates Renesas’ exclusive Dynamically Reconfigurable Processor (DRP), which achieves real-time image processing at low power consumption. This allows applications incorporating embedded devices – such as smart appliances, service robots, and compact industrial machinery – to carry out image recognition employing cameras and other AI functions while maintaining low power consumption and accelerating the realisation of intelligent endpoints.

Currently, there are several challenges to using AI in the operational technology (OT) field, such as difficulty transferring large amounts of sensor data to the cloud for processing, and delays waiting for AI judgments to be transferred back from the cloud. Renesas already offers AI unit solutions that can detect previously invisible faults in real time by minutely analysing oscillation waveforms from motors or machines. To accelerate the adoption of AI in the OT field, Renesas has developed the RZ/A2M with DRP, which makes possible image-based AI functionality requiring larger volumes of data.

Since real-time image processing can be accomplished while consuming very little power, battery-powered devices can perform tasks such as real-time image recognition based on camera input, biometric authentication using fingerprints or iris scans, and high-speed scanning by handheld scanners. This solves several issues associated with cloud-based approaches, such as the difficulty of achieving real-time performance, assuring privacy, and maintaining security.

Author
Amy Best