In the world of embedded systems, Artificial Intelligence (AI) is quickly becoming a core component of many devices. From smart sensors and AI-powered cameras to autonomous vehicles and industrial automation, AI is driving the next wave of innovation. However, embedding AI into devices requires powerful, efficient, and scalable hardware solutions. This is where System on Modules come into play.
In this article, we will explore why SoM modules are the best choice for embedded AI applications, and how they provide distinct advantages for manufacturers and developers. We’ll also highlight how companies like Geniatech are leveraging SoM technology to push the boundaries of embedded AI systems.
What is a System on Module?
A System on Module is a compact, integrated circuit board that contains most of the core components of an embedded system, such as a processor (CPU or SoC), memory, storage, I/O interfaces, and sometimes even networking capabilities. The beauty of a SoM lies in its ability to provide a pre-integrated, plug-and-play solution that developers can use to design customized products. Unlike traditional custom designs that require assembling components from scratch, SoMs save time and effort by offering a unified solution.
In the context of embedded AI applications, SoM modules typically feature specialized hardware like AI accelerators, Neural Processing Units (NPUs), and GPUs to support complex machine learning models and real-time AI processing.
- High AI Processing Power
Embedded AI applications often demand significant computational resources to handle machine learning models, data processing, and real-time decision-making. SoM modules are designed to meet these needs by integrating powerful AI accelerators, NPUs, and GPUs.
For instance, many modern SoM modules, such as those powered by ARM Cortex-A or RK-series processors, come equipped with built-in AI processing units that can handle image recognition, voice processing, sensor fusion, and other demanding AI tasks with ease. These modules are optimized for AI workloads, allowing real-time inferencing directly at the edge, reducing the dependency on cloud processing and improving performance.
Geniatech’s RK3576 SoM, for example, integrates a powerful 6TOPs NPU that supports AI algorithms like image recognition, voice recognition, and natural language processing. This makes it ideal for AIoT devices, smart cameras, and other applications that require edge-based AI computation.
- Power Efficiency for Edge Computing
One of the major challenges in embedded AI applications is achieving a balance between performance and power consumption. Many AI tasks require high-performance processors, which often come with high power demands. This becomes a concern when deploying AI systems in battery-powered or energy-sensitive applications, such as remote sensors, wearables, and autonomous systems.
SoM modules are specifically designed for low-power consumption, making them perfect for edge devices that need to operate continuously without draining power. The energy-efficient processors and AI accelerators in SoMs enable developers to achieve high levels of AI performance while keeping power usage in check. Many SoM solutions, like Geniatech’s SoM platforms, are built with low-power technologies like LPDDR memory, energy-efficient processors, and advanced power management features to maximize battery life.
- Accelerated Time-to-Market
In the rapidly evolving field of embedded AI, the speed at which products are developed and brought to market is critical. Traditional custom designs involve extensive prototyping, integration, and testing, which can delay product launches and increase development costs.
SoM modules provide a pre-integrated solution that can significantly reduce the design and development time. Developers can simply connect a SoM to their carrier board and focus on integrating the necessary peripherals and software. This plug-and-play approach allows manufacturers to accelerate time-to-market for AI-powered products.
- Scalability and Flexibility
AI applications often evolve over time, and the ability to scale or upgrade a system is crucial. One of the key benefits of SoM modules is their scalability. SoMs offer flexibility for manufacturers to design products with varying levels of performance and features while maintaining a consistent platform for future upgrades.
SoMs allow developers to choose a module that fits their current requirements and easily upgrade it in the future as AI technologies evolve. For example, as AI models become more complex or as new AI accelerators become available, developers can simply swap out the existing SoM for a more powerful version, without needing to redesign the entire system.
- Compact and Integrated Design
Embedded AI applications often require systems that are not only powerful but also compact and efficient in terms of space. SoM modules are small and highly integrated, which makes them ideal for applications with limited space, such as wearable devices, drones, smart cameras, and automotive applications.
The integration of all core system components—CPU, memory, storage, I/O interfaces, and AI accelerators—into a single module reduces the footprint of the embedded system. This compact design allows manufacturers to create sleek, efficient, and powerful devices that can be deployed in a wide range of environments.
- Easy Integration with Peripherals
Embedded AI systems often require integration with various sensors, cameras, displays, and other peripherals. SoM modules are designed to make these integrations as simple as possible. Many SoMs come with a variety of I/O interfaces, such as USB, GPIOs, PCIe, Ethernet, and HDMI, to support a wide range of peripherals.
This flexibility allows developers to quickly design AI-powered devices that interact with the real world, whether it’s through AI cameras for facial recognition or IoT sensors for environmental monitoring. The ease of integration makes SoM modules particularly attractive for AI-driven IoT applications.
- Ecosystem and Software Support
Another reason why SoM modules are ideal for embedded AI applications is the ecosystem and software support they come with. Most SoM platforms, such as those offered by Geniatech, provide a complete development kit, including software libraries, SDKs, and technical support, to help developers quickly get started with AI application development.
With pre-configured AI frameworks and built-in support for popular AI libraries like TensorFlow, PyTorch, and OpenCV, SoMs provide a solid foundation for building AI-powered products. Developers can easily access tools to build, train, and deploy machine learning models, allowing them to focus on building innovative AI solutions rather than dealing with low-level system integration.
Conclusion
As the demand for embedded AI applications grows, SoM modules are emerging as the best solution for meeting the performance, power, scalability, and flexibility needs of these systems. By offering powerful AI processing capabilities, low power consumption, rapid development timelines, and easy integration with peripherals, SoM modules are empowering manufacturers and developers to build next-generation AI devices.
Whether you’re working on AIoT solutions, autonomous systems, or smart devices, SoM modules provide the ideal platform for creating high-performance embedded AI applications. Companies like Geniatech offer a wide range of SoM solutions that are designed to meet the diverse needs of the AI industry, providing developers with the tools they need to bring their AI products to market quickly and efficiently.
By leveraging SoM technology, you can accelerate your AI-powered embedded system development and stay ahead in this fast-evolving industry.
