Renesas and Fixstars collaboration targets deep learning development

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Renesas Electronics, a supplier of advanced semiconductor solutions, and Fixstars Corporation, a developer of multi-core CPU/GPU/FPGA acceleration technology, are to collaborate on automotive deep learning applications.

In April 2022, the two companies hope to establish an Automotive SW Platform Lab tasked with the development of software and operating environments for Renesas automotive devices. The lab will support early development and ongoing operation of advanced driver-assistance systems (ADAS) and autonomous driving (AD) systems. The two companies will also develop technologies aimed at software development for deep learning, in addition to building operating environments that have the ability to continuously update learned network models to maintain and enhance recognition accuracy and performance.

“Fixstars possesses both advanced software technology for deep learning and optimization technology that allows more efficient utilization of hardware,” said Takeshi Kataoka, SVP and general manager of the Automotive Solution Business Unit at Renesas. “I am confident that our collaboration will enable us to provide strong support for software development optimized for automotive applications and allow our customers to fully leverage the superior performance of Renesas’s automotive devices.”

As part of their collaboration, the companies have launched Genesis for R-Car, a cloud-based evaluation environment for R-Car that supports early development of ADAS and AD systems. The new environment facilitates instant initial evaluations when selecting devices. It utilizes the Genesis cloud-based device evaluation environment from Fixstars as its platform.

“After developing a deep learning application, it is not possible to maintain high recognition accuracy and performance without constantly updating it with the latest learning data,” added Fixstars CEO Satoshi Miki. “Fixstars plans to focus on these machine learning operations (MLOps) for the automotive field, as we work together with Renesas to develop a deep learning development platform optimized for Renesas devices.”

The companies highlight that poring over specifications is time consuming and inefficient. Evaluation based on actual use cases is essential when selecting devices. To do this, users typically need to obtain an evaluation board and basic software to evaluate devices, and technical expertise is required in order to build an evaluation environment. However, the Genesis for R-Car cloud-based evaluation environment does not require specialized technical expertise.

It allows engineers to confirm the processing execution time in frames per second (fps) and recognition accuracy percentage of R-Car V3H’s CNN accelerators on sample images using generic CNN models, such as ResNet or MobileNet. It also allows them to select the device and network they wish to evaluate and perform operations remotely on an actual board.

The Genesis environment can then be used to confirm evaluation results in tasks such as image classification and object detection, giving developers the option to use their own images or video data. This simplifies the initial evaluation to determine whether R-Car V3H is suitable for the customer’s system. Future plans include the rollout of a service that will allow customers to use their own CNN models for evaluations.

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Lawrence has been covering engineering subjects – with a focus on motorsport technology – since 2007 and has edited and contributed to a variety of international titles. Currently, he oversees Automotive Powertrain Technology and Professional Motorsport World magazines as editor.

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