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Real-Time Analytics on MCUs & MPUs

Real-Time Analytics & 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 on 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.

Try Reality AI Explorer to experience firsthand how Reality AI Tools can help you develop AI and TinyML solutions in industrial, automotive, and commercial applications.

Technical Advantages

Fully Integrated 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.

Small Footprint for Speed & Accuracy

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 with Model 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.

Reality AI Software Solutions

Reality AI Tools®

Automatically explore sensor data and generate optimized models

RealityCheck™ AD

Anomaly detection for monitoring factory and process-industry assets

Automotive Seeing with Sound (SWS)

Combine hardware and software to give passengers a new level of protection

RealityCheck™ HVAC Solutions Suite

Complete framework for smart, self-diagnosing HVAC systems

RealityCheck™ Motor Toolbox

Advanced software toolbox enabling predictive maintenance and anomaly detection

Reality AI Utilities

Plug-in module for Renesas e² studio

Resources

Documentation

Type Title Date
White Paper PDF 2.20 MB
White Paper PDF 951 KB
Flyer PDF 679 KB
White Paper PDF 655 KB
White Paper PDF 875 KB 日本語
White Paper PDF 717 KB
6 items

Videos & Training

Reality AI Overview

See how you can use Reality AI software and tools to develop products using sensors and machine learning on low-power, general purpose microcontrollers from Renesas. Learn more at renesas.com/ai

News & Blog Posts

Increase Motor Performance and Reduce Stress with Sensorless Load Detection on Three-Phase BLDC/PMSM Motors Blog Post Oct 16, 2024
Empowering Developers with Free Access to Advanced AI/ML Development Tools Blog Post Jul 16, 2024
New Reality AI Explorer Tier Offers Free Access to Comprehensive Evaluation “Sandbox” of Powerful AI/ML Development Environment News Jul 16, 2024
Design AI/ML Applications the Easy Way Blog Post Mar 13, 2024
Renesas Extends Its AIoT Leadership with Integration of Reality AI Tools and e² studio IDE News Sep 21, 2023
How to Maximize the Lifespan of Electric Motors Blog Post Jun 29, 2023
FFTs and Stupid Deep Learning Tricks Blog Post Aug 31, 2022
Peaks and Valleys: How Data Segmentation Can Conserve Power and CPU Cycles in Edge AI Systems Blog Post Aug 30, 2022
How Do You Make AI Explainable? Start with the Explanation Blog Post Aug 29, 2022
Bias Isn’t Always Bad Blog Post Aug 26, 2022
Want to Reduce the Cost of Data Collection for Edge AI with Sensors? Only Do It Once. Blog Post Aug 25, 2022
What is a Sensor, Anyway Blog Post Aug 17, 2022
What’s Wrong with My Machine Learning Model? Blog Post Aug 17, 2022
Successful Data Collection for Machine Learning with Sensors Blog Post Aug 16, 2022
Embedded AI – Delivering Results, Managing Constraints Blog Post Aug 16, 2022
Edge AI – Difference Between a Project and a Product Blog Post Aug 16, 2022
Comprehensive AI Engineering Software for Making Smart Edge Devices with Sensors Blog Post Aug 15, 2022
3 Ways to Make Your Machine Learning Projects Successful Blog Post Aug 12, 2022
It’s All About the Features Blog Post Aug 12, 2022
Rich Data, Poor Data: Getting the Most Out of Sensors Blog Post Aug 12, 2022
5 Tips for Collecting Machine Learning Data from High-Sample-Rate Sensors Blog Post Aug 11, 2022

Tools & Resources