瑞萨博客

端点AI，即在网络边缘设备上集成人工智能。目标是使基于机器学习的智能决策在物理上更接近数据源，因此AI ML向嵌入式端点转移。

本系列将分期介绍瑞萨开发出品的32位MCU RX软件组的历史和前景，这一软件组旨在实现低代码和无代码开发应用程序。第五期将为大家介绍的是RX系列的现代软件开发方法。

Embracing open RAN initiatives, Renesas has joined the movement to enable emerging and existing wireless RAN OEMs with innovative RF, timing, power management, optical, and low-power edge AI solutions.

Imagine the challenge created by managing the power requirements of a data center with hundreds of servers, each of which may have thousands of multi-core processors that draw hundreds of amps per core and must be turned on and off in a matter of nanoseconds. All of that means the corresponding power solutions have to be agile, versatile and scalable. Renesas’ digital power technologies are the solution.

In this blog, we describe the important parameters in evaluating TinyML systems, explaining the correlation between the metrics and the current industry benchmark standards for TinyML

瑞萨电子于8月17日在福瑞泰克杭州总部举办了技术研讨会，并与福瑞泰克研发团队分享了ADAS/AD方面的产品和解决方案。

An overview of how Renesas’ Voice User Interface solutions for the edge and endpoint can help you easily add voice commands to your next IoT design

近十年来，Renesas电子一直是多个功能安全工作组的积极成员，并为相关的功能安全标准做出了众多贡献。机器学习/人工智能的演进和更高计算性能硬件的发展，正在不断推动更高效的安全解决方案——无论是防止系统故障还是预测和减少未知风险，采用哪种人工智能模型也是工程师在设计功能安全系统时的一个关键考虑因素。

In this blog we demonstrate Edge AI solutions for the kinds of real engineering problems that our customers use Reality AI software for every day, running on real hardware they might actually deploy.