Automotive

Renesas Electronics Accelerates NCAP Front Camera Application Development for Entry and Mid-Range Cars with Comprehensive R-Car V3M-Based Solution

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R-Car V3M Starter Kit Significantly Reduces Development Efforts and Accelerates Time-to-Market

R-Car V3M Now Includes CNN Accelerator to Deliver Ultra-Low Power Consumption for High-Performance Convolutional Neural Networks in NCAP Camera Applications

 Renesas Electronics Corporation (TSE: 6723), a premier supplier of advanced semiconductor solutions, today announced the R-Car V3M Starter Kit to simplify and speed up the development of New Car Assessment Program (NCAP, Note 1) front camera applications, surround view systems, and LiDARs. The new starter kit is based on the R-Car V3M image recognition system-on-chip (SoC), delivering a combination of low power consumption and high performance for the growing NCAP front camera market. By combining the R-Car V3M starter kit with supporting software and tools, system developers can easily develop front camera applications, contributing to reduced development efforts and faster time-to-market.

Renesas also announced an enhancement to the R-Car V3M by integrating a new, highly power-efficient hardware accelerator for high-performance convolutional neural networks (CNNs, Note 2), which enables features such as road detection or object classification that are increasingly used in automotive applications. The R-Car V3M’s innovative hardware accelerator enables CNNs to execute at ultra-low power consumption levels that cannot be reached when CNNs are running on CPUs or GPUs.

The new R-Car V3M Starter Kit, the R-Car V3M SoC, and supporting software and tools including Renesas’ open-source e² studio IDE integrated development environment (IDE), are part of Renesas’ open, innovative, and trusted Renesas autonomy™ Platform for ADAS and automated driving that delivers total end-to-end solutions scaling from cloud to sensing and vehicle control.

“With the R-Car V3M Starter Kit, Renesas significantly reduces development efforts and accelerates time-to-market for the rapidly growing market of NCAP cameras,” said Jean-Francois Chouteau, Vice President, Renesas Electronics Corporation. “Following our announcements on the R-Car V3M SoC and our partnership with HELLA Aglaia for front camera application software, the starter kit completes the offering to develop state-of-the-art front camera solutions.”

Key features of the R-Car V3M Starter Kit

  •  Flexible board can be employed for a wide range of computing applications

The new starter kit is a ready-to-use kit. In addition to the required interface and tools, the kit provides essential components for ADAS and automated driving development, including 2GB RAM, 4GB eMMC (embedded multi-media controller, Note 3) onboard memory, Ethernet, display outputs, and interfaces for debugging. The integrated 440-pin expansion port gives full freedom for system manufacturers to develop application-specific expansion boards for a wide range of computing applications, from a simple advanced computer vision development environment to prototyping of multi-camera systems for applications such as surround view. This board flexibility reduces the time needed for hardware development in addition to maintaining a high degree of software portability and reusability.

  • Quick start solution by Linux BSP and various tools

The R-Car V3M Starter Kit is supported by a Linux Board Support Package (BSP), which is available through elinux.org. Further commercial operating systems will be made available from next year onwards. Codeplay will enable OpenCL™ and SYCL™ on the starter kit in 1Q 2018. Further tools, sample code and application notes for computer vision and image processing will be provided throughout 2018. Renesas enables several tools on the R-Car V3M Starter Kit including Renesas e2Studio toolchain and tools for debugging, which ease the development burden and enable faster time-to-market. Partners, including Cogent Embedded that developed a 3D surround view parking assist system, are making their field-proven software solutions and development systems available using the R-Car V3M Starter Kit to accelerate development.

In addition to the R-Car V3M Starter Kit, Renesas has enabled ultra-low power consumption for CNNs, which achieve image recognition and image classification, on the R-Car V3M SoC. The R-Car V3M allows the implementation of high-performance, low power consumption CNN networks in NCAP cameras that cannot be realized with traditional high power consuming CPU or GPU architectures. Renesas complements the IMP-X5, a subsystem for computer vision processing that is composed of an image processor and the programmable CV engine, with a new, innovative CNN hardware accelerator developed in house, that allows the implementation of high-performance CNNs at ultra-low low power. With this new IP, Renesas enables system developers to choose between the IMP-X5 or the new hardware accelerator to deploy CNNs. This heterogeneous approach allows system developers to choose the most efficient architecture, depending on required programming flexibility, performance, and power consumption.

Availability

The Renesas R-Car V3M is available now. The R-Car V3M Starter Kit with a Linux BSP will be available in Q1 2018 initially in limited quantities. A complete offering with an extended software solution is scheduled for Q3 2018.

Please refer to the separate sheet for product specifications of the R-Car V3M Starter Kit.

(Note 1) New Car Assessment Program (NCAP): A government car safety program tasked with evaluating new automobile designs for performance against various safety threats.

(Note 2) Convolutional neural networks (CNNs), are deep, feed-forward artificial neural networks that have been successfully applied for analyzing visual imagery and are increasingly used in automotive for applications like road detection or object classification.

(Note 3) An embedded multi-media controller (eMMC) onboard memory allows to boot the operating system from the starter kit. This is much faster than booting via network.

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