A System-on-Module (SOM) provides the core components of an embedded processing system — including processor cores, communication interfaces, and memory blocks — on a single production-ready printed circuit board (PCB). This modular approach makes a SOM ideal for embedding into end systems, from robots to security cameras.
The concept of System-on-Modules grew out of blade servers. These thin servers were created with the goal of saving storage space and minimizing power consumption. The lean design mentality behind blade servers carries over to the architecture of SOMs. They include only the components required for their intended function in as small a package as possible, and they’re flexible enough to accommodate a wide range of applications.
SOMs are distinct from a system-on-a-chip (SoC). An SoC, as its name implies, is a collection of key computer components placed on a single chip. While SOMs may include an SOC, they are board-based, and as such have the space to include additional components.
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SOMs make the impossible possible for developers, reducing time-to-market and keeping costs down. Creating an embedded system is typically a lengthy process, requiring custom board design and manufacture. A SOM streamlines the steps needed to bring a design to fruition. Simply choose a SOM that fits your needs, integrate it into your end system, and you’re ready for deployment. Beyond enabling high volume deployment, module-based designs simplify product life cycle management and reduce bill of material (BOM) expenses.
Whether your focus is software, hardware, or even AI development, SOMs are worth a close look:
SOMs are being used worldwide. Here’s a look at just a few of their applications.
Cutting edge security camera systems take advantage of video analytics, and SOMs make it happen. Video analytics-powered security cameras use machine learning to categorize and understand what they see, providing a stream of accurate data in real time. This wouldn’t be possible without edge computing and the ability for cameras and other devices to analyze information on site.
The modern economy relies on machine vision for everything from inventory inspection to signature recognition to defect detection. Machine vision requires embedded systems that can analyze data on the spot and offer configurable sensor capabilities. SOMs enable developers to take advantage of machine vision at scale while keeping costs low.
Smart cities employ ubiquitous sensors to gather data and provide the insights decision-makers need to keep their communities functioning and vibrant. SOMs power these sensors and help city officials stay on top of everything from utilities to traffic without missing a beat.
Xilinx’s Kria™ KV260 Vision AI Starter Kit is specifically intended for developers who haven’t yet experienced the benefits of module-based design and aren’t sure where to start. Xilinx is committed to bringing enhanced efficiency and reduced risk to developers globally. It supports its offerings with an ever-growing selection of accelerated applications — made available to all users through its marketplace — designed to meet key SOM use cases. By reducing the need for you to write code on your own, Xilinx enables you to deliver your end systems more quickly and explore markets you otherwise wouldn’t have the chance to.
Get started with SOMs today by taking a look at Xilinx’s Kria SOM portfolio, which includes all the information and training materials you need to begin your SOM journey.
Specifically designed for developers who haven’t yet experienced the benefits of module-based design and aren’t sure where to start. Supports an ever-growing selection of Accelerated Applications through the Xilinx App Store. Deliver your end systems more quickly and explore market opportunities.
The KR260 Robotics Starter Kit integrates high-performance industrial interfaces and features native ROS 2 support. Designed for robotics and industrial applications, the KR260 is the fastest way to develop intelligent factory solutions for production volume deployment with the K26 SOM.