Choosing and Training the Right AI Models for the Edge

However, deploying these applications is no easy task. Compared to traditional hardware, size-constrained embedded systems have limited resources. Success, therefore, depends on selecting the appropriate model architecture and using the right training techniques to balance operational requirements with the technical limitations of edge hardware.

The Journey from Raw Data to a Trained Model

Developing an effective edge AI model requires a well-defined pipeline that transforms raw information into a deployable solution.

This journey begins with data collection. Before any training can occur, an AI model needs high-quality data that is representative and complete. It's crucial to consider not only where the data will come from but also how it will be preprocessed, stored, and secured. Once collected, this data must be given meaning through labeling, where it is manually or semi-automatically annotated with tags or categories. This process allows the model to understand its inputs and helps to mitigate bias and improve accuracy.

With labeled data prepared, the focus shifts to selecting the model architecture that best fits the hardware and use case. Key options include:

• Convolutional Neural Networks (CNNs), which specialize in spatial data and are primarily used for image processing, computer vision, and object detection.
• Recurrent Neural Networks (RNNs) are designed to process sequential or temporal data. An RNN's ability to remember previous inputs makes it ideal for tasks like natural language processing (NLP) and forecasting.
• TinyML, which uses various architectures to create models specifically built to operate on devices with extremely low memory and power, such as microcontrollers. These are often used for predictive maintenance or real-time object and sound detection.

The selected architecture is then brought to life during the training phase, an iterative process where the model gradually learns to recognize and extrapolate patterns from the labeled data. This allows it to become increasingly accurate when making classifications or decisions based on new information.

However, training is not the final step. To ensure the model can perform in the real world, it undergoes rigorous validation, tuning, and testing. A portion of the data is held back during training to evaluate the model's ability to generalize and to tune it against issues like overfitting. Finally, it is tested with another set of unseen data that serves as a final measure of its performance, accuracy, and bias.

The Critical Role of High-Quality Data

An AI model is only as good as the data it’s trained on, a principle that is especially true for edge AI. Because of limited memory, processing power, and energy resources, edge models are typically smaller and have a much lower tolerance for error than traditional models. High-quality, relevant data allows the model to learn meaningful patterns and makes it possible to train compact models ideal for the constraints of edge hardware.

Achieving this requires a thorough approach to data preparation. This continuous process involves defining collection strategies, identifying all possible scenarios for the model, and iteratively assessing the data as it is collected.

Clean, balanced datasets are also essential to prevent the model from learning irrelevant or biased features. The data must be preprocessed to eliminate duplicates, errors, and noise, while also correcting for inconsistencies. From there, the most relevant features are isolated, inputs are standardized, and the dataset is expanded and balanced through techniques like augmentation.

The Balancing Act of Choosing a Model Architecture

Selecting an edge AI model architecture is a delicate balancing act between accuracy, inference speed, resource availability, flexibility, and specialization. A complex architecture like a large CNN might offer high accuracy but at the cost of more compute power and memory. Conversely, lightweight architectures like TinyML models are fast and efficient but may require sacrificing some model performance. Similarly, a general-purpose model may be more broadly applicable but require more resources than a specialized model, which is more efficient but less adaptable.

These trade-offs must be weighed with hardware limitations kept front-of-mind. For instance, a model like YOLO is purpose-built for computer vision and offers scalable sizes from the full-featured YOLOv8 to the lightweight YOLOv10-Nano, suiting different hardware capabilities. In contrast, models from Hugging Face, an open-source platform of large language models, excel at NLP and are well-suited for multimodal applications. Another option, Mistral, offers great flexibility with support for many development languages, making it ideal for contextual analysis.

Keeping Hardware Limitations in Mind

Hardware limitations should be kept front-of-mind during decision-making, as edge devices and models must be appropriately matched in terms of memory, processing power, and energy use. Our earlier blog on Choosing the Right AI for Video Security illustrates how this process works in practice.

SECO simplifies this balancing act by offering a broad portfolio of edge AI hardware and software platforms tailored for a range of performance, power, and form factor requirements. From high-efficiency modules to powerful industrial computers, SECO enables developers to match the right AI model to the right system.

Development kits, reference designs, and integration-ready solutions streamline the path from concept to deployment—making it faster and easier to bring AI-powered products to market.

Final Optimizations for Peak Performance at the Edge

Because edge environments are resource-constrained, optimizing a model's size and complexity is essential for it to operate effectively. Several common techniques help achieve this:

• Quantization shrinks a model’s size and improves its speed by reducing the precision of its numerical values—for example, by converting 32-bit floating-point values to 8-bit integers.
• Pruning further reduces complexity by identifying and removing redundant or inactive segments of the neural network.
• Specialized compression and deployment tools such as TensorFlow Lite, ONNX, and PyTorch assist with the conversion, optimization, and deployment of these lean models onto edge devices.

Conclusion

The potential of edge AI is significant, but unlocking it requires the right training data, model architecture, and hardware. Organizations must carefully balance their business goals and operational needs against the resource constraints inherent in edge computing.

Whatever your use case, SECO can help you implement AI at the edge quickly, efficiently, and effectively. Contact us today to learn how you can enter this rapidly growing market.