Renesas Blog

This blog explains how Renesas views the evolution of the automotive industry and builds its business strategy.

For model-based development, I would like to introduce Embedded Target for RH850 Virtual Platform, which facilitates early verification and shortening of development period.

全球领先的半导体解决方案供应商瑞萨电子，与专注于自动驾驶领域系统解决方案供应商知行科技，于11月8日在苏州举行了战略合作框架协议签约。

In this blog, the RH850 Virtual Platform (VPF) function to improve the safety and quality of automotive software is introduced.

We will introduce a multi-device synchronous debug & trace tool (released in September 2022) that enables synchronized execution, break control, and acquisition of trace information for multiple SoCs and MCUs.

Software is becoming the new sensor. This shift in thinking opens the door to incorporating more complex, AI-based algorithms, rather than just simple condition thresholds.

Relying only on high-level descriptive statistics rather than time and frequency domains will miss anomalies, fail to detect signatures and sacrifice value that an implementation could potentially deliver.

Real-time streaming data must be carved into smaller windows for consideration by a machine learning model, how that stream is carved up can have an impact on model performance and power consumption.

As sensor and MCU costs decreased, an ever-increasing number of organizations have attempted to exploit this by adding sensor-driven embedded AI to their products.

The more sophisticated machine learning tools that are optimized for signal problems and embedded deployment can cut months, or even years, from an R&D cycle.