
RL78 8/16-bit microcontrollers (MCUs) greatly improve power efficiency with industry-leading low power consumption at 37.5μA/MHz consumption during normal operation and 0.355μA during clock operation. Built-in features such as a high-precision (±1%) high-speed on-chip oscillator, background operation data flash capable of 1 million rewrites, temperature sensor, and interface ports for multiple power supplies help reduce system costs and size.
Renesas offers the Product Longevity Program (PLP) to help customers select the most appropriate part for applications that require long life cycles.
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Type | Title | Date |
Guide | PDF 188 KB | |
Brochure | PDF 11.44 MB 日本語 | |
Brochure | PDF 8.04 MB 日本語 | |
Product Change Notice | PDF 633 KB 日本語 | |
Other | PDF 3.19 MB 日本語 , 简体中文 | |
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This video introduces how to do AI coding using ChatGPT and the RL78 Arduino-compatible board, including practical examples such as lighting up a 7Seg LED, rotating a motor, and sensing with a temperature and humidity sensor. Even beginners can easily experience AI coding!
Detailed step-by-step instructions:
AI Coding with Chat-GPT for RL78 Microcontroller (Arduino) | Renesas
Related products: RL78/G23-64p FPB
Related sources: Quick Start Guide page (GitHub)
Disclaimer:
1. Informational Purposes Only: This video serves as a general guide to using ChatGPT/GPT-4 to write code for an Arduino RL78 evaluation board. Renesas does not guarantee the accuracy or completeness of the content provided.
2. Third-Party Rights Infringement: Note that code generated using ChatGPT/GPT-4 might potentially infringe third-party intellectual property rights. Renesas makes no representations or warranties that the use of ChatGPT/GPT-4 will not violate any third-party rights. Users are urged to exercise due diligence to ensure compliance with applicable laws and third-party rights.
3. No Liability: In no event shall Renesas be liable for any damages, data loss, or other adverse consequences that may occur as a result of using the information, code, techniques, or tools demonstrated in this video. You may want to apply industry standard static and dynamic code assessments as AI code has been recognized to possibly contain malware fragments as well as deriving other unwanted outcomes.