2019 LEAP Award bestowed on Renesas Electronics for leading-edge microprocessor

The votes are in. Business-to-Business publisher WTWH Media, LLC, has announced the winners of the second-year Leadership in Engineering Achievement Program (LEAP) Awards, a design engineering product competition honoring the most exciting new products in a host of technology areas: Additive Manufacturing, Advanced Materials, Analog Electronics, Connectivity, Embedded Computing, Fastener Technology, Hydraulics, Industrial Automation, Mechanical, Motion Control, Pneumatics, Power Electronics, Software, Switches & Sensors, and Test & Measurement.

Honors in the Embedded Computing category went to Renesas Electronics Corporation for their RZ/A2M MPU which combines Renesas proprietary DRP technology for fast pre-processing of image data and feature extraction, running alongside an Arm Cortex-A9 CPU for decision making, to deliver a unique hybrid approach for accelerating machine vision applications. This integration makes the RZ/A2M MPU ideal for embedded applications that require real-time image processing, low power consumption, and small form factor.

The DRP built into the MPU is run time-reconfigurable hardware that delivers the acceleration benefits of dedicated circuitry while avoiding the cost and power penalties associated with embedded FPGA-based approaches. DRP technology breaks through logic capacity constraints by dividing complex data paths into smaller “Contexts” that are loaded under the direction of a state transition controller to reconfigure the accelerator hardware on a cycle-by-cycle basis without interrupting CPU execution. This Dynamic Configuration capability realizes spatial expansion of the acceleration resources, enabling the DRP silicon to implement functions of varying complexity.

Dynamic Loading of new configuration data sets enables the DRP to accelerate multiple algorithms within the RZ/A2M application code. The combination of Dynamic Configuration and Dynamic Loading gives the DRP hardware virtually unlimited expansion capability, eliminating the need to move to a larger device as algorithms grow in complexity. Upgrades to address evolving standards or enhance functionality can even be delivered to systems in the field via software updates.