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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ビデオ&トレーニング

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.

ニュース&ブログ

FFTs and Stupid Deep Learning Tricks ブログ 2022年8月31日
Peaks and Valleys: How Data Segmentation Can Conserve Power and CPU Cycles in Edge AI Systems ブログ 2022年8月30日
How Do You Make AI Explainable? Start with the Explanation ブログ 2022年8月29日
Bias Isn’t Always Bad ブログ 2022年8月26日
Want to Reduce the Cost of Data Collection for Edge AI with Sensors? Only Do It Once. ブログ 2022年8月25日
国内初!AIによる工場向け異常検知ソリューション「RealityCheck AD」の提供を開始 ブログ 2022年8月17日
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Successful Data Collection for Machine Learning with Sensors ブログ 2022年8月16日
Embedded AI and Machine Learning - Adding New Advancements in the Tech Space ブログ 2022年8月16日
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Rich Data, Poor Data: Getting the Most Out of Sensors ブログ 2022年8月12日
5 Tips for Collecting Machine Learning Data from High-Sample-Rate Sensors ブログ 2022年8月11日