瑞萨博客

本文介绍了多设备同步调试和跟踪工具（已于 2022 年 9 月推出），可实现同步运行、中断控制并获取多个 SoC 和 MCU 的跟踪信息。

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.

This post offers tips on collecting data from high-sample-rate sensors for use with machine learning.

2022 年 9 月，瑞萨发布了客户与合作伙伴之间的直接沟通工具。客户可从 R-Car 联盟合作伙伴获取高水平解决方案建议，旨在解决客户难题并加速汽车开发。

In this blog, we will present our work on solutions to facilitate the creation of remote development environments used in in-vehicle software development.

通过瑞萨汽车用 16 位微控制器 RL78 安全解决方案（免费样例软件、瑞萨安全解决方案入门套件），助力高效开发！