Real-Time Analytics and Non-Visual Sensing

Edge AI and TinyML have paved the way for enterprises to build smart product features that use machine learning running on highly constrained edge nodes.

Reality AI is an Edge AI software development environment that combines advanced signal processing, machine learning, and anomaly detection on every MCU/MPU Renesas core. The software is underpinned by the proprietary Reality AI ML algorithm that delivers accurate and fully explainable results supporting diverse applications. These include equipment monitoring, predictive maintenance, and sensing user behavior as well as the surrounding environment – enabling these features to be added to products with minimal impact to the BoM.

Reality AI software running on Renesas processors will help you deliver endpoint intelligence in your product offering and support your solutions across all markets.

Technical Advantages

Full Integration with Renesas Toolchain

The Reality AI software comes with integration to Renesas e2studio, plus support for all Renesas cores and MCU dev boards. Integration with Renesas Motor Control kits is available as an add-on option.

Speed and Accuracy, with a Small Footprint

Unlike approaches that use quantization, compression, pruning or other machine learning techniques that make models small but erode accuracy, Reality AI combines advanced signal processing methods with machine learning that deliver full accuracy in a tiny footprint without compromises.

Transparency and Explainability

No engineer will deploy a solution they don't understand, so Reality AI offers transparency into model function based on time and frequency, as well as full source code available in C or MATLAB. You can always explain to colleagues and stakeholders why models perform as they do, and why they should be trusted.

Cost Optimization

Instrumentation and data collection are >80% of the cost of most machine learning projects, and Reality AI has analytics that can help reduce the cost of both. Reality AI Tools® can identify the most cost-effective combinations of sensor channels, find the best sensor locations, and generate minimum component specifications. It can also help you manage the cost of data collection by finding instrumentation and data processing problems as data is gathered.

Documentation

Title Type Date
PDF540 KB
Guide
PDF1.55 MB
Product Brief
PDF3.08 MB
Product Brief
PDF284 KB
Product Brief
PDF3.85 MB日本語
White Paper
PDF486 KB
White Paper

Videos & Training

IoT World 2022: Reality AI Sensorless Predictive Maintenance Demo

During the IoT World 2022 in Austin, Texas, Renesas showcased a sensorless predictive maintenance use case powered by Reality AI’s machine learning tools that enable a complete Auto-ML framework for real-time analytics applications. Watch the demos in action and a full interview by TECH TV, featuring Kaushal Vora (Senior Director of Business Acceleration and Ecosystem) and Nalin Balan (Business Development Manager). Visit renesas.com/ai to learn more about our AI solutions and technologies.