Renesas & Fixstars Develop Simulation Tools for AD and ADAS

By Tiera Oliver

Associate Editor

Embedded Computing Design

December 27, 2022


Renesas & Fixstars Develop Simulation Tools for AD and ADAS

TOKYO, Japan ― Renesas and Fixstars Corporation are jointly developing tools for autonomous drive and advanced driver-assistance systems (ADAS) that enable software simulation and optimization, specifically for the R-Car system-on-chip (SoC) devices from Renesas.

The R-Car tools are designed to provide reliable object recognition for network model development at the beginning of the software development phase, therefore lessening the time to market and the chances of post-development revisions.

“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.”

Renesas and Fixstars have developed the following tools to meet of today’s AD and ADAS applications:

1. R-Car Neural Architecture Search (NAS) tool for generating network models optimized for R-Car

This tool generates deep learning network models that efficiently utilize the CNN (convolutional neural network) accelerator, DSP, and memory on the R-Car device. This allows engineers to rapidly develop lightweight network models that achieve highly accurate object recognition and fast processing time even without a deep knowledge or experience with the R-Car architecture.

2. R-Car DNN Compiler for compiling network models for R-Car

This compiler converts optimized network models into programs that can make full use of the performance potential of R-Car. It converts network models into programs that can run quickly on the CNN IP and also performs memory optimization to enable high-speed, limited-capacity SRAM to maximize its performance.

3. R-Car DNN Simulator for fast simulation of compiled programs

This simulator can be used to rapidly verify the operation of programs on a PC, rather than on the actual R-Car chip. Using this tool, developers can generate the same operation results that would be produced by R-Car. If the recognition accuracy of inference processing is impacted during the process of making models more lightweight and optimizing programs, engineers can provide immediate feedback to model development, therefore shortening development cycles.

The joint “Automotive SW Platform Lab” established by the companies is designed to support early and ongoing ADAS and autonomous drive developments. Through the lab, Renesas and Fixstars will develop software for deep learning, and build operation environments to improve object recognition through continuously updated network models.  

The first set of tools are available and designed for the R-Car V4H SoC for AD and ADAS applications.

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Tiera Oliver, Associate Editor for Embedded Computing Design, is responsible for web content edits, product news, and constructing stories. She also assists with newsletter updates as well as contributing and editing content for ECD podcasts and the ECD YouTube channel. Before working at ECD, Tiera graduated from Northern Arizona University where she received her B.S. in journalism and political science and worked as a news reporter for the university’s student led newspaper, The Lumberjack.

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