Renesas Blog

Renesas uses a machine learning guided process to explore a huge variety of well-understood mathematical and engineering feature spaces.

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.

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.

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。

Reality AI Tools focuses on "inherent explainability" — keeping the fundamental functioning of each model conceptually accessible to the design engineer from the first steps of model construction.

The RISC-V platform continues to gain interest and momentum, the Renesas RISC-V ASSP Easy devices lower the barriers to entry with turnkey reference solutions for quick time to market.

Bias in a technical, statistical sense can be a good thing – or at least a useful thing – so long as you recognize it, understand the effect it has and manage it.

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

Reality AI Tools^® includes functionality to monitor the status of data collection automatically, tracking consistency, quality, and coverage.