PSOC™ Edge is designed for responsive compute and control applications, featuring hardware-assisted machine learning (ML) acceleration. Enabling end products to be even more intuitively usable and adding contextual awareness. By combining high performance M55/U55 ML cores with very low power M33/NNLite cores, PSOC Edge can support “Always On” intuitive awareness in smart home, security, wearables, robotics and more.

Edge AI is no longer a science project—it’s a design constraint. Whether you’re building safety-aware robots, smarter factories, intelligent drones, connected medical and IoT devices, you need silicon, tools, and partners that move you from idea to production fast.
In this 90-minute session, NXP will present a practical, system-level view of our AI/ML-enabled portfolio—from low-power MCUs to high-performance MPUs and discrete NPU accelerators. You’ll learn how to combine these building blocks with NXP’s eIQ® ML software suite, Time Series Studio, and a rich set of available partner tools to develop and deploy real-world AI workloads such as anomaly detection, predictive maintenance, vision, and sensor fusion.
We’ll cover real-world architectures, device-selection guidelines, and development best practices so you can match performance, power, and cost targets while streamlining your ML workflow from data to deployed models.
You’ll leave with clear design patterns, reference resources, and a practical roadmap for bringing robust, production-ready Edge AI solutions to market faster and with lower risk.

Edge AI refers to the enabling technology that runs AI algorithms and models directly on devices such as microcontrollers, microprocessors, and sensors embedded in industrial and automotive applications. Deploying AI at the edge enables real-time data processing directly at the source of data collection, offering faster response times, enhanced data security, and greater bandwidth efficiency.

The ST Edge AI Suite is a set of tools for integrating AI features in embedded systems. It supports STM32 microcontrollers and microprocessors, Stellar automotive microcontrollers, and MEMS smart sensors, and includes resources for data handling and AI model optimization and deployment. Users will also find educational insights and real-world case studies to simplify their design journey.

The ST Edge AI Suite facilitates the deployment of AI models by allowing users to easily find the right tool for their project:

• Data logging: capturing the sensor data necessary for AI model training.
• Auto ML: automatically generating optimized machine learning algorithms.
• Model optimization: optimizing AI models and generating associated code for target devices.
• Validation and testing: ensuring model performance meets deployment criteria.
• Online benchmarking: testing model performance on ST hardware using the cloud.

Embedded developers can also benefit from:

• The model zoo: simplifying the deployment of AI models on supported devices.
• Documentation for more guidance through the deployment process.

This session introduces Renesas’ approach to practical, deployable edge AI, built around three core pillars: Real-Time Analytics, Voice, and Vision. Attendees will gain a high-level understanding of how TinyML and edge AI can be applied on Renesas platforms to add intelligence directly at the edge—where latency, power, and reliability matter most.

Attendees will learn how to deploy TinyML on real Renesas MCUs and MPUs to enable always-on intelligence with ultra-low power and deterministic performance.

See real-world AI use cases brought to life across:

• New Energy – predictive maintenance, anomaly detection, energy optimization
• Home & Building Automation – voice control, occupancy detection, smart sensing
• Industrial Automation – condition monitoring, quality inspection, real-time analytics
• Home Appliances – smarter, more responsive user experiences with edge AI.

Understand when and how to use Real-Time Analytics vs. Voice vs. Vision, and how to combine them in a single system architecture. Get practical design guidance on model selection, memory footprint, latency, and power optimization for edge AI. .Walk away with concrete implementation ideas you can immediately apply to current or upcoming designs using Renesas platforms and tools