Renesas Blog

Reality AI Tools 4.0 allows customers to use artificial intelligence to reduce the cost of developing, procuring and manufacturing smart devices.

Machine learning projects can be successful through understanding ground truth, curating the data and not overtraining a machine learning model.

With the right features, almost any machine learning algorithm will find what you're looking for. Without good features, none will.

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.

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

Introducing RZ/N2L, a companion chip for industrial Ethernet and TSN with hassle-free, easy connectivity and rich peripheral functions.

Are you facing difficulties meeting functional safety requirements in your automotive system development? If so, I would like to introduce you to our functional safety system solution for xEV inverter applications that simplifies implementation and shortens the development time.

This blog describes how Renesas' automotive BMS application models & software can reduce development time for our customers.

RX23E-A RSSK has started its support for Lab on the Cloud. You can evaluate the noise performance of a high-precision delta-sigma ADC built in the RX23E-A online.

The endpoint describes any Device that is physically at the end of any point in the network. While applying AI at the endpoint can have many benefits, securing it can be difficult. The fact that endpoints are considered the entrance to IoT systems, makes them very attractive targets for hackers. Thus, security principles have to be incorporated into the architectural design process of IoT systems.