Overview

Reality AI Tools® allows engineers to generate and build TinyML/Edge AI models based on advanced signal processing. Users can automatically explore sensor data and generate optimized models. Reality AI Tools contains analytics to find the best sensor or combination of sensors, locations for sensor placement, and automatic generation of component specs and includes fully explainable model functions in terms of time/frequency domains, and optimized code for Arm® Cortex® M/A/R implementations.

AI ExploreAI Explore
Automated Feature Exploration/Model Generation

BOM OptimizationBOM Optimization
Use AI to find the most cost-effective components

Data ReadinessData Readiness
Understand the state of training and testing

Edge AI/TinyMLEdge AI/TinyML
Super-compact, efficient code for the smallest MCUs

Optional Add‑onsOptional
Add-ons
Reality AI for MATLAB or Radar

Synergy Download

Documentation

Type Title Date
Product Brief
Reality AI Tools® Product Brief
PDF 416 KB
White Paper
Data Collection for Edge AI with Sensors
PDF 717 KB
Application Note
e2 studio Integration - Getting Start Guide using RA6T2
PDF 16.95 MB
White Paper
Case Study: Renesas' RealityCheck™ Motor Software Toolbox
PDF 628 KB 日本語 , 简体中文
Product Brief
RealityCheck™ AD Product Brief
PDF 560 KB
Guide
Ultimate Guide to Machine Learning for Embedded Systems
PDF 540 KB
White Paper
AI as a Service for Signal Processing
PDF 3.85 MB 日本語
7 items

Design & Development

Related Pages
RealityCheck™ AD for Industrial Anomaly Detection
RealityCheck™ AD is a ready-to-implement anomaly detection solution for monitoring factory and process-industry assets.
Downloads
RealityCheck™ AD Product Brief
 Solution Toolkit Renesas
Automotive Sound Recognition (SWS)
The Reality AI solution uniquely combines both hardware and software to give passengers a new level of protection.
 Sound / Voice Renesas
2 items
Reality AI / e2 studio Integration Overview Video

An overview of how the integration of Renesas Reality AI Tools® and e2 studio IDE can simplify your development cycle and for AI / ML edge and endpoint applications. Visit the Reality AI software page for more details or download e2 studio to interact with the Reality AI Tools.

Video List
Reality AI / e2 studio Integration Overview Video
Generative AI is happening at the Edge and Endpoint
AI Systems Need Integrated Software and Hardware Solutions
AI & Embedded Workflow Integration
RealityCheck™ HVAC Demo Overview
Embedded AI TinyML-based Sensorless Predictive Maintenance for Electric Motor Systems
Predictive Maintenance and Condition Monitoring Demo - Built with Reality AI Tools™
Reality AI Overview
What is Reality AI?
Reality Bytes #1: What is Edge AI?
Reality Bytes #2: How to put AI on a Microcontroller
Reality Bytes #3: What are Features?
Building products using TinyML on Arm MCUs | Reality AI

Events & Webinars

News & Blog Posts

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
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

AI-Driven Feature Discovery

Features are mathematical descriptions of "things that matter" for purposes of telling the difference between classes, predicting a variable, or detecting anomalies.

Features searched by Reality AI Tools® include:

  • Common transforms on raw data, including logs, powers, derivatives, signs, and more
  • Parametric statistical features and peak analysis
  • Spectral features, including power, phase, spectral shape, periodicity, cepstral, wavelet, etc.
  • Linear and non-linear dimensionality reduction
  • Time-frequency sparse coding and time pattern analysis
  • Binary pattern and texture analysis
AI-Driven Feature Discovery

 

Automatically Explores Sensor Data and Generates Optimized Models

How Reality AI Tools Works

 

AI Explore

AI Explore automatically finds alternative solutions and tells you what it found.

AI Explore

Additionally, the processing requirements for each possible solution can be shown so you can make necessary trade-offs.

AI Explore - Shows processing requirements for each possible solution.

 

Explainable AI

With Explainable AI, specific class signatures can be inspected in terms engineers understand.

Explainable AI

 

Sensor Selection

Identify the best performing sensors, and the best, most cost-effective locations.

Sensor Selection

 

Setting Specifications

Use AI to set minimum component specifications.

Measurement Error Tolerance
Scaled Noise Sensitivity
Sample Rate Sensitivity

 

Automated Monitoring

The cheapest problems to fix are the ones you catch early. Reduce the cost of data collection through automated monitoring for common pitfalls.

Data Distribution - Category Coverage
Data Distribution - File Consistency
Data Quality
Class X Capacity

 

Easy Integration

Easily integrate Reality AI Tools with your firmware build. Reality AI Tools supports Arm® Cortex® M, R and A architectures from all major manufacturers, as well as Linux and Windows. Many non-Arm architectures are also supported. Ask us about your target processor.

Easy Integration with Reality AI Tools

 

Add-ons for Reality AI Tools

Reality AI for MATLAB

Read MATLAB data files, and use Reality AI to generate models for the MATLAB Signal Processing and Machine Learning toolboxes. Full transparency of MATLAB code - See the details of optimized feature computations and machine learning models generated by Reality AI.

Reality AI for Radar

Automatically select and optimize radar pre-processing options for greater model accuracy. For professional developers working with radar.

In-vehicle AI

 

Use Cases

Reality Time Analysis

Reality Time Analysis

Use our analytics engine to support hardware design (not just algorithms and model building)

 

Anomaly Detection

Anomaly Detection

Reality AI software helps you detect anomalies by learning normal behavior. You can use the built-in anomaly detection and condition monitoring dashboard, or create your own.

 

Predictive Maintenance & Remaining-Useful-Life

Predictive Maintenance & Remaining-Useful-Life

Predict the remaining useful life of components (filters, etc.), identify operational conditions, and detect abnormal conditions.

 

Sensorless Sensing with Motor Control

Sensorless Sensing with Motor Control

Develop predictive maintenance applications with no addition to the Bill of Materials - only a firmware upgrade to the motor control board.