Renesas and Fixstars to Collaborate in Automotive Deep Learning with Establishment of Joint Lab

Cloud Based Environment for Automotive R Car SoCs

The new Lab will support early development and ongoing operation of advanced driver-assistance systems (ADAS) and autonomous driving (AD) systems. The two companies will develop technologies aimed at software development for deep learning and building operating environments that have the ability to continuously update learned network models to maintain and enhance recognition accuracy and performance.

As part of their collaboration, today Renesas and Fixstars are launching 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.

Poring over specifications is time consuming and inefficient. Evaluation based on actual use cases is essential when selecting devices. Users typically need to obtain an evaluation board and basic software to evaluate devices, and technical expertise is also required in order to build an evaluation environment. The new GENESIS for R-Car cloud-based evaluation environment does not require specialized technical expertise.

GENESIS for R-Car lets engineers 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 engineers to select the device and network they wish to evaluate and perform operations remotely on an actual board. Engineers can use the GENESIS environment to confirm evaluation results in tasks such as image classification and object detection, with the option to use their own images or video data. This greatly 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.