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Overview

Description

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.

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.

Try Reality AI Explorer

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

Target Devices

Documentation

Design & Development

Sample Code

Related Boards & Kits

Explore

Videos & Training

News & Blog Posts

Revolutionizing HVAC with AI-Powered Motor Control Blog Post Dec 17, 2024
Build Advanced Voice User Interfaces with Enhanced Recognition, Anti-Spoofing and Speaker Identification Blog Post Nov 21, 2024
Increase Motor Performance and Reduce Stress with Sensorless Load Detection on Three-Phase BLDC/PMSM Motors Blog Post Oct 16, 2024
The Future of Digital Motor Control: Multiple Motors, Embedded AI, and Advanced Algorithms on One MCU Blog Post Sep 19, 2024
Is Your Vacuum Smart Enough to Clean for Real? Blog Post Sep 12, 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
Can Your Doorbell Be Spoofed? Blog Post Jun 1, 2024
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
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Automatically Explores Sensor Data and Generates Optimized Models

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How Reality AI Tools Works

 

AI Explore

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

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AI Explore

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

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AI Explore - Shows processing requirements for each possible solution.

 

Explainable AI

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

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Explainable AI

 

Sensor Selection

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

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Setting Specifications

Use AI to set minimum component specifications.

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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.

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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.

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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.

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In-vehicle AI

 

Use Cases

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Reality Time Analysis

Reality Time Analysis

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

 

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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.

 

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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.

 

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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.