Companies jointly develop tools suite R-Car SoCs

Renesas Electronics Corporation and Fixstars Corporation are jointly developing a suite of tools that enables optimisation and fast simulation of software for AD systems and ADAS specifically designed for Renesas' R-Car SoC devices. These tools make it feasible to rapidly develop network models with extremely accurate object recognition from the initial stage of software development that carries the advantage of the performance of the R-Car. This decreases post-development rework and helps shrink development cycles.

"Renesas continues to create integrated development environments that enable customers to adopt the 'software-first' approach," said Hirofumi Kawaguchi, vice president of the Automotive Software Development Division at Renesas. "By supporting the development of deep learning models tailored to R-Car, we help our customers build AD and ADAS solutions while also reducing the time to market and development costs."

"The GENESIS for R-Car, which is a cloud-based evaluation environment that we built jointly with Renesas, allows engineers to evaluate and select devices earlier in the development cycles and has already been used by many customers," said Satoshi Miki, CEO of Fixstars. "We will continue to develop new technologies to accelerate machine learning operations (MLOps) that can be used to maintain the latest versions of software in automotive applications."

Today's AD and ADAS applications use deep learning to accomplish highly accurate object recognition. Deep learning inference processing needs massive amounts of data calculations and memory capacity. The models and executable programs on automotive applications must be optimised for an automotive SoC since real-time processing with limited arithmetic units and memory resources can be challenging. In addition, the process from software evaluation to verification must be accelerated, and updates must be applied repeatedly to improve accuracy and performance. The companies have developed the following tools designed to satisfy these needs.

R-Car Neural Architecture Search (NAS) tool for generating network models optimised for R-Car; R-Car DNN Compiler for compiling network models for R-Car; and R-Car DNN Simulator for fast simulation of compiled programs.

They will continue developing deep learning software with the joint 'Automotive SW Platform Lab' and build operation environments that maintain and improve recognition accuracy and performance by continually updating network models.