The challenges of embedded AI development
AI is finding its way into an increasing number of embedded applications, from industrial equipment and automotive systems to aerospace, radar and advanced instrumentation. In these environments, processing often needs to happen directly on the device, close to the sensors and data sources, rather than relying on a remote server or cloud infrastructure.
Putting AI into an embedded system, however, comes with a number of practical constraints. Engineers need to consider processing performance, power consumption, latency and connectivity, while ensuring that all hardware and software components work together reliably.
Take a vision system as an example. It may need to process several video streams, handle sensor inputs and communicate with other equipment at the same time. Radar systems face similar demands, with large volumes of data that must be processed quickly and consistently.
A standard processor can handle many of these tasks, but it may not always be the most suitable option for workloads requiring high data throughput or predictable response times. Dedicated AI accelerators can help with inference, while programmable logic remains valuable for signal processing, data handling and application-specific functions.
Choosing the right architecture is therefore an important part of the design process. The aim is to combine processing resources and programmable hardware in a way that meets the application’s requirements without adding unnecessary complexity. AMD Versal™ adaptive SoCs offer an approach to addressing these different needs within a single platform.
AMD Versal™ and Vulcan: bridging AI technology and embedded development
AMD Versal™ adaptive SoCs provide a flexible foundation for developing embedded systems that combine processing, programmable logic and high-speed connectivity. Rather than relying on a single type of processing resource, Versal devices bring together different computing capabilities within one adaptive architecture.
This approach allows engineers to distribute workloads according to their specific requirements. Processing tasks, data handling and hardware-accelerated functions can be organised to support the needs of the target application.
AMD offers different Versal™ families to address different workloads. Versal™ Prime provides a balance of processing performance, connectivity and programmable logic, making it suitable for demanding embedded applications involving intensive data processing and low-latency operation. Versal™ AI Edge, meanwhile, extends this architecture with dedicated AI Engines designed to accelerate machine learning workloads at the edge, including applications such as computer vision and sensor fusion.
For developers looking to turn these capabilities into an operational embedded system, hardware integration is another important consideration. Designing a complete board around an advanced SoC requires careful work on power management, memory, connectivity, thermal management and software support. This can add significant complexity and extend development schedules.
This is where Vulcan, the System-on-Module developed by reflex ces, provides a practical starting point.
Available in two configurations based on AMD Versal™ technology, Vulcan gives developers the flexibility to select the architecture that best matches their application requirements:
- Vulcan with AMD Versal™ Prime VM2202: designed for applications requiring programmable logic, intensive signal processing, connectivity and deterministic data handling.
- Vulcan with AMD Versal™ AI Edge VE2802: adds 304 dedicated AI Engines to support accelerated AI processing and inference workloads at the edge.
Both configurations share a common module concept and integrate key system components, including the adaptive SoC, memory and high-speed interfaces. The module also features dual Arm Cortex-A72 and dual Cortex-R5F processing cores, alongside PCIe Gen4 connectivity and LPDDR4 memory.
By providing these components on a pre-integrated and tested platform, Vulcan helps reduce the hardware design effort involved in building an embedded system.
With Vulcan, the objective is not to impose a single architecture on every project, but to provide a choice of AMD Versal-based configurations within a common platform approach.
Turning AI innovation into a real-world solution means bringing together algorithms, hardware and software in a system designed around the needs of its application. Whether it is enabling machines to detect anomalies on a production line, helping vehicles interpret their surroundings, supporting real-time analysis in medical and scientific equipment, or processing complex signals in Electronic Warfare and radar systems, the objective is the same: make AI capabilities useful, reliable and operational in the field.
Achieving this requires choosing the right architecture, integrating the necessary processing capabilities and validating the system under real operating conditions. With the right development approach, AI can move beyond experimentation and become an integral part of embedded products, creating new opportunities across industries, including defense and aerospace.