As edge AI continues to transform industries—from smart cities and autonomous vehicles to factory automation and remote monitoring—hardware architecture plays a crucial role in determining success. Developers are constantly faced with a key decision: should they build around a System on Module or a System on Chip?
While both approaches deliver powerful compute capabilities, they differ significantly in flexibility, integration, and scalability. Choosing the right one impacts everything from time-to-market and production cost to thermal design and long-term maintainability.
This article explores the trade-offs and use cases behind the ongoing System on Module vs System on Chip debate in edge AI deployments.
What Is the Difference Between a System on Module and System on Chip?
A System on Chip (SoC) integrates CPU, GPU, memory controller, and sometimes wireless connectivity—all on a single silicon die. This high level of integration reduces size and power consumption, making SoCs ideal for space-constrained devices like smartphones, wearables, or low-power IoT nodes.
In contrast, a System on Module (SoM) is a small, plug-in board that includes an SoC (or processor), memory, storage, power management, and I/O interfaces. It’s designed to connect to a baseboard or carrier that provides application-specific functionality. This modular design separates processing from the hardware interface layer, enabling more design freedom.
In essence:
- SoC = highly integrated silicon chip
- SoM = modular computing platform built around an SoC
Design Flexibility: Modular Integration vs Fixed Layout
One of the biggest advantages of using a SoM is its flexibility. Developers can tailor the baseboard to specific application needs—adding or removing I/O ports, adjusting form factors, or integrating custom sensors—while keeping the processing module unchanged.
This modular separation:
- Allows faster prototyping and testing
- Supports rapid customization for different markets
- Makes hardware upgrades easier without full redesigns
With an SoC-only solution, any design change typically requires a full PCB re-layout and thorough revalidation. This fixed integration reduces design flexibility, which may be a limitation in projects that expect rapid iteration or multiple product variants.
Scalability and Reusability Across Product Lines
A well-designed SoM architecture enables easy performance scaling across product tiers. For example, a single baseboard can host multiple compute modules—from entry-level to high-end—depending on the target use case.
This makes SoMs ideal for product families that share a common design foundation but differ in compute demands, such as:
- Basic and AI-enhanced versions of a gateway
- Entry-level vs premium edge vision devices
- Standard vs ruggedized industrial controllers
With an SoC-based solution, such scalability often requires separate board designs, increasing engineering effort and time.
Power Efficiency and Thermal Considerations
System on Chip designs tend to be more power-efficient due to their highly integrated nature and optimized silicon. This is critical for battery-powered and thermally constrained environments.
However, many SoMs are now built around energy-efficient SoCs and offer optimized power management frameworks. For edge AI devices operating in industrial or fanless enclosures, SoMs also provide thermal design flexibility—developers can adapt heat sinks or enclosures to match performance requirements.
In scenarios with ultra-low power demands, SoCs may still offer an edge. But for balanced compute and flexibility, modern SoMs often meet the requirements effectively.
Time-to-Market and Development Complexity
When building from a raw SoC, developers must handle everything: board layout, power management, memory integration, and firmware development. This requires in-depth hardware expertise, longer development cycles, and more rigorous testing.
By contrast, SoMs are typically production-ready. They come with:
- Pre-validated hardware
- Software BSPs for Linux or Android
- Reference carrier boards
- Detailed documentation and support
This enables faster development and accelerates prototyping—especially valuable in markets with tight schedules or frequent design iterations.
Software Support and Ecosystem Compatibility
For AI-driven embedded projects, reliable software support is essential. SoMs often include ready-to-use board support packages (BSPs), SDKs, and sometimes AI frameworks compatible with popular inference engines like TensorFlow Lite or ONNX.
SoCs may also be supported—especially if they’re from major vendors like NXP, Qualcomm, or Rockchip—but software maintenance often falls on the developer when using a bare SoC.
In addition, SoMs may offer long-term kernel support, OTA update frameworks, and real-time OS support for industrial-grade deployments.
Cost Considerations: Volume, Customization, and Lifecycle
Cost is an important factor when comparing System on Module and System on Chip.
- SoCs offer a lower BOM cost at high volumes, making them ideal for mass-market consumer devices. However, up-front engineering investment is higher.
- SoMs cost more per unit, but save on development time, reduce NRE (non-recurring engineering) costs, and support small-to-medium production runs more efficiently.
SoMs also offer a lifecycle advantage. Many vendors provide 5- to 10-year availability, important for industrial and medical applications where requalification is costly.
Application Fit: When to Choose SoM or SoC in Edge AI Projects
Choose a SoM when:
- You need fast prototyping and shorter time-to-market
- Your product family requires scalability
- I/O needs vary or may evolve
- You’re building industrial or AI-enabled embedded devices with moderate-to-low volumes
Choose a SoC when:
- You’re developing ultra-compact or power-constrained devices
- Cost per unit is a top priority at very high volumes
- Your design is stable, and you control the full board layout
- You have in-house expertise in embedded board development
For most edge AI applications requiring AI inference at the edge, multimedia, or complex I/O, SoMs provide a balanced, low-risk path to production.
Geniatech’s Approach: Scalable AI Hardware for Every Use Case
Geniatech offers a robust range of embedded compute solutions—whether you’re building around a SoM or an SoC-based kit.
Key highlights include:
- SoMs based on NXP, Rockchip, and Qualcomm platforms
- Support for OSM, SMARC, Qseven, and custom form factors
- AI acceleration support via Hailo, Kinara, and NVIDIA Jetson
- Carrier board customization and long-lifecycle support
Our products are designed to help developers go from prototype to production quickly, reliably, and cost-effectively.
Conclusion: Choosing the Right Path for Edge AI Success
The System on Module vs System on Chip decision doesn’t have a one-size-fits-all answer. It depends on your design goals, team expertise, product lifecycle, and market timeline.
If your priority is customization, scalability, and development speed, SoMs offer an adaptable and efficient solution. If size and cost dominate your constraints, and you have the resources for full custom design, SoCs may be the way to go.
In edge AI applications where demands are evolving and time-to-market is critical, SoMs remain a powerful enabler—bridging flexibility with performance.
