Embedded AI

We help you introduce AI in embedded systems. AI brings tasks that would typically require human intelligence, such as visual perception, speech recognition, problem-solving, and decision-making.

Machine learning (ML), a subset of AI, focuses on algorithms and statistical models that allow machines to adapt and improve from data (experience) without being explicitly programmed. A machine learning model is trained on a dataset and based on that training. It can make observations or predictions about new, unseen data. Machine learning is an essential component in developing AI systems. In embedded systems, ML can be used for various applications, such as:

Predictive Maintenance: Using historical data to predict equipment failures and schedule maintenance proactively.

Anomaly Detection: Identifying unusual states and behaviors in system operations, which could indicate potential issues.

Optimization: Enhancing performance and efficiency of processes through continuous memorizing and adaptation.

Deep learning (DL), a subset of machine learning, utilizes artificial neural networks with multiple layers to process and learn from vast amounts of data. It aims to mimic the structure and functionality of mechanism of analyze of thought.

Embedded AI is the application and integration of AI capabilities, ML and DL. It enables them to learn from their environment, adapt to user preferences and autonomously respond to changes. AI models for embedded systems must be relatively small in size considering they are to be run on microcontrollers, and other low-power processors. The training of these models is done by capturing the data from sensors and training it over a cloud platform before finally transferring it to the embedded device. The device then consumes real-time data from the sensors and applies the model to that data.

Embedded AI model sticks to following points:

Runs on a microcontroller, or a low-power microcomputer with limited RAM, flash memory and sometimes even battery power.

The embedded system, also called the edge device can execute the AI model without any need for data communication over the network. The network should only be used to transfer results and enrich the data of the AI model.

AI learning model should have a minimal footprint (a few tens of kilobytes), so it can be run on microcontrollers that have limited memory and computing resources.

By deploying AI algorithms directly on the edge, the response time is significantly reduced resulting in improved real-time performance, achieved by removing the delays imposed by transmitting data with remote servers. Embedded AI provides also decision-making.

Embedded AI applications can be:

Real-time predictive maintenance – preemptively makes critical maintenance decisions before a disruptive event can occur, schedule maintenance, repairs, and avoid unexpected failures. It contributes to lower maintenance costs, downtime reduction, increased lifespan and maximized ROI. Some applications of predictive maintenance include water utility leak detection, appliance failure detection and equipment predictive maintenance.

Computer Vision – embedded AI systems can be used to perform object detection, facial recognition and image classification in real-time, for example, granting people/objects access or calling security for blacklisted people/objects. A major concern for camera usage in such applications, where personal data is collected through video recordings and then processed, is GDPR which aims to ensure that individuals have control over their personal information. That means companies should be careful and ensure the responsible and ethical use of the personal data captured.

Sound Detection – to detect potential anomalies.

Real-time Decision Making – Embedded AI systems can process data and make decisions in real-time, crucial for applications like autonomous driving, where timely decisions are critical for safety.

Sensor Data Fusion – Technique that combines data from multiple sensors to generate a more accurate and reliable understanding of the environment than what could be achieved using individual sensors alone. It enhances accuracy, robustness, and extended coverage. These advantages not only improve the performance AI systems but also contribute to improve decision-making processes.