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.


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News & Blog Posts

Renesas Extends Its AIoT Leadership with Integration of Reality AI Tools and e² studio IDE News Sep 21, 2023
Semiconductor Industry Is Pulling AI Across a Diversity of End Uses and Applications Blog Post Jul 24, 2023
Renesas Picks Its Battle on Edge AI News Jul 5, 2023
How to Maximize the Lifespan of Electric Motors Blog Post Jun 29, 2023
Renesas Updates Progress One Year After Reality AI Acquisition News Jun 15, 2023
Renesas Round-Up: A Q&A with Roger Wendelken on Trends Shaping Industrial MCUs Blog Post Mar 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
Solutions for Real Problems Running on Cortex-M4 and M7 Platforms 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 and Machine Learning - Adding New Advancements in the Tech Space 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