Automotive SoC software development from SDK to cloud

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Another chipmaker is taking its semiconductor design solutions to the cloud in a bid to reinvent the developer experience while easing evaluation tasks for deep learning-based automotive designs. Together with software company Fixstars, Renesas will establish the Automotive SW Platform Lab next month to help optimize software development for advanced driver assistance systems (ADAS) and autonomous driving applications. Fixstars specializes in software solutions built around CPU/GPU/FPGA multi-core acceleration technology.

Automotive developers are increasingly turning to deep learning to find new ways to activate cameras and sensors in ADAS and autonomous driving systems. However, software development and delivery is a major problem for automotive system developers, especially for deep learning designs that have been primarily designed for consumer and server applications.

Figure 1 Automotive SW Platform Lab will provide evaluation services for deep learning camera and sensor designs built around the R-Car chips. Source: Renesas

Renesas, with Fixstars, embeds deep learning designs built around its R-Car system-on-chip (SoC) in the cloud to facilitate instant initial assessments when selecting chips for ADAS and autonomous vehicle designs . Renesas will use Fixstars’ cloud-based device evaluation environment, GENESIS, for the early development of deep learning-based automotive applications.

From SDK to cloud

It is worth mentioning that Renesas has already made available the R-Car Software Development Kit (SDK) to enable faster and easier software development and validation for automotive designs. The software platform, delivered in a single package, is designed for automotive computer vision and rules-based artificial intelligence (AI) functions. The SDK includes a comprehensive set of sample software, popular CNN networks, and application notes.

Figure 2 The SDK’s e² studio added new features, including bus monitoring support and debugging capabilities for image processing and deep learning subsystems. Source: Renesas

While design engineers commonly use evaluation boards and associated software to evaluate devices, technical expertise is still required to create an evaluation environment. On the other hand, a cloud-based evaluation environment, such as that offered by Renesas, does not require specialized technical expertise.

Then, a cloud-based platform allows developers to confirm processing execution time in frames per second (fps) and recognition accuracy percentage of CNN accelerators on sample images using generic CNN models such as ResNet and MobileNet. Additionally, designers can use the cloud platform to confirm assessment results in tasks such as image classification and object detection with the ability to use their own image or video data. This, in turn, allows engineers to determine if the chip is suitable for the system design.

Satoshi Miki, CEO of Fixstars, points out that the cloud platform for automotive SoCs is also crucial because after developing a deep learning application, it is not possible to maintain high recognition accuracy and performance. without constantly updating it with the latest training data. The cloud-based design platform allows developers to continuously update learned network models to maintain and improve recognition accuracy and performance.

When it unveiled the SDK for R-Car chips in September 2021, Renesas pledged to follow through with a virtual platform. Here it comes with a set of cloud-based assessment tools, promising to simplify software development for automotive designs serving ADAS and autonomous vehicles in passenger, commercial and off-road environments.

Majeed Ahmad, editor of EDN and Planet Analog, has covered the electronics design industry for over two decades.

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