Machine Learning from Variscite: SOMs for data analysis and machine vision with the help of AI

In recent years, machine learning (Machine Learning) has become critically important in many sectors of the economy. This technology enables computers to autonomously acquire knowledge from available data and make decisions based on identified patterns. In an era of rapid data growth in areas such as industry, medicine, transportation, or finance, the use of ML methods is becoming indispensable. It allows for the automatic extraction of valuable insights from extensive datasets, surpassing traditional analytical models in terms of efficiency and effectiveness.

The Growing Role of Machine Learning

How Does the Machine Learning Process Work?

Key stages include:

Data Analysis – first, a dataset is gathered, then it is filtered and undergoes preliminary processing to enable better forecasting.

Model Evaluation – to assess prediction accuracy, algorithms use an error function that determines how much the result deviates from expectations.

Optimization – in the final step, the model is improved by modifying parameters and retraining the algorithm.

By cyclically repeating these steps, each iteration increases prediction accuracy and enhances the model’s ability to adapt to changing conditions.

Main Streams of Machine Learning

Supervised learning – we train the algorithm on labeled data so that it can learn to classify or predict outcomes.

Unsupervised learning – the data is unlabeled; this method focuses on discovering hidden patterns and relationships in the dataset.

Semi-supervised learning – combines elements of the two previous techniques, using both labeled and unlabeled data.

Reinforcement learning – employs a “trial-and-error” strategy, where the algorithm aims to achieve a defined goal by systematically adjusting its actions.

Applications of Machine Learning

Modern ML-based solutions are used in various industries – from IoT and transportation to agriculture and industrial production. Examples include:

Image Analysis – automatic recognition and classification of objects.

Failure Forecasting – analyzing signals from machines to predict malfunctions and minimize downtime.

Process Optimization – improving production efficiency and reducing operational costs.

Service Personalization – adapting offers to customer preferences in real time.

One solution that enables rapid data processing and accelerates AI/ML algorithms is Variscite’s product range. The DART-MX95 model, equipped with a powerful NPU (Neural Processing Unit), is an excellent foundation for projects requiring real-time image analysis.

What Is Machine Vision?

Machine Vision, also referred to as visual inspection, involves capturing and analyzing images or video clips to identify objects and their features. By leveraging ML algorithms and artificial intelligence, these systems can interpret data with high accuracy—often surpassing human capabilities. Machine Vision is used in quality control systems, monitoring, and robotics. A prime example is autonomous vehicles, which observe their surroundings in real time and make appropriate decisions, thereby enhancing safety and driving comfort.

Variscite’s AI-Focused Portfolio

DART series (with pin2pin compatibility)

DART-MX95: Thanks to a powerful NPU and ISP, it is an outstanding choice for intelligent vision systems and high-resolution cameras.

DART-MX93: Equipped with a dual-core Cortex-A55 and an NPU, it is ideal for transportation applications, such as video analysis from surveillance cameras.

DART-MX8M-PLUS: Boasts a quad-core Cortex-A53 and an 800 MHz Cortex-M7. Its NPU significantly accelerates tasks tied to AI/ML processing, crucial for production automation, for instance.

DART-MX8M: Designed for mobile applications and HMI interfaces. It supports 2D/3D graphics and enables the implementation of machine learning for gesture recognition or user behavior analysis.

VAR-SOM series (also pin2pin)

VAR-SOM-MX93: Well-suited for IoT solutions, combining a dual-core Cortex-A55 with an NPU and fast data transfer (Wi-Fi 6).

VAR-SOM-MX8M-PLUS: Thanks to its high computing power and advanced NPU, it fits medical applications, for example, in diagnostic image analysis.

Python API (pyvar)

With developers in mind, Variscite has created a Python library (pyvar) that significantly simplifies developing ML and multimedia applications on SoM modules. Thanks to ready-to-use classes and examples, novice programmers can quickly begin working with AI algorithms. Integration with NXP i.MX8 processors makes deploying new solutions more intuitive while reducing the time needed to launch a project.

Summary

Machine learning and machine vision technologies are transforming numerous sectors, offering faster, more accurate, and more efficient data analysis. Variscite, in cooperation with Soyter Components, provides a wide range of SoM modules (both from the DART and VAR-SOM series) that allow companies to successfully implement AI/ML projects in embedded systems, boosting competitiveness and enhancing the quality and efficiency of their processes.