边缘 AI 技术

Explore embedded AI with Saud as he reveals methods for optimizing models on low‑power MCUs and MPUs. Learn how quantization, heterogeneous computing, and advanced optimization techniques make real‑time analytics, vision, and voice possible at the edge. Saud also demonstrates how Renesas solutions like RUHMI and Reality AI Tools streamline deployment for developers working under tight resource constraints.

Explore the enhanced functionalities in Reality AI Tools and the new features integrated into Renesas e² studio. Reality AI Utilities reduce the complexity of creating lightweight ML models while improving and stabilizing results. These tools enable embedded engineers to work directly with AI tools.

A look at how data-driven engineering tools can learn from examples to make a decision and ultimately be deployed automatically.

Did you know most day-to-day processing operations actually occur at the edge or endpoint of the network? Find out more about what is happening in the Edge AI space.

To make AI systems truly work, it is crucial to have a well-integrated combination of specialized AI software and hardware components. Watch to learn more about why this is so important.

Focused on the practical application of TinyML in embedded systems, this webinar demonstrates how Renesas low-power general-purpose MCUs paired with Reality AI Tools® can bring intelligence to sensor-driven products. Use cases covered include sound recognition and localization, non-visual gesture recognition across capacitive, radar, and motion inputs, and anomaly detection in smart industrial equipment. Learn how the Reality AI Tools edge AI software environment supports the full embedded machine learning development workflow, from data capture through to real-time analytics deployment in MCU-based systems — making it an accessible entry point for designers looking to add AI capabilities to their next product.

Brought together by Renesas and Avnet Electronics, this hands-on virtual session explores how the RZ/V2L-based Single Board Computer simplifies the development of embedded Vision AI systems at the edge and endpoints. Hear directly from engineers on how the compact, dual-core Arm Cortex-A55 based SBC — powered by the Renesas RZ/V2L MPU with integrated AI accelerator and Vision features — delivers best-in-class power efficiency at under 5W. Learn how the SBC can accelerate system design timelines and explore the available options for incorporating it directly into a product. A practical session for developers looking to fast-track their next Vision AI design.