3 Reasons to Use a SOM For Your Next AI Project
The rising prominence of artificial intelligence (AI) has challenged developers to create devices that are increasingly capable of understanding and responding to information in real time. What separates AI from your standard processing applications is the ability to think, learn from experience, and operate with minimal human intervention. The complex systems and massive quantities of data necessary to implement AI effectively often require the implementation of a network of devices working in tandem to collect, interpret, and respond to information.
AI seldom functions alone. In most cases, your AI will have IoT connectivity capabilities to harvest and process data from numerous inputs. IoT devices essentially constitute a network of sensors and internet connected devices that wirelessly collect data. Where your IoT device network gathers and transmits useful data, the AI brain is capable of interpreting it and dispatching a response.
Most consumer AI/IoT devices will utilize chip-down circuit board development, then store and process information in the connected cloud. The approach has its advantages, especially in consumer applications. The cloud delivers unrivaled scalability and immensely powerful systems, but this comes with a cost. When your devices rely on a network of connectivity and cloud components, the resulting lag time can slow your device when seconds count. Furthermore, each transmission invites security concerns that can be bypassed by embedding AI directly. Most importantly, universal use of the cloud diminishes the capacity for device by device personalization.
Advances in edge computing now allow devices to utilize onboard AI without relying on a cloud network. Edge devices are those on the front lines of your network, that interact directly with the users. The circuit board requirements for edge computation are daunting but can be fulfilled by groundbreaking System on Modules (SOMs). SOMs can be leveraged to jumpstart time to market and realize the full benefits of bringing AI functions directly to the user. SOMs deliver the processing power, reliability, and versatility to give your device an edge over those that are reliant on the cloud for their AI functions.
When AI processing takes place locally, information has less distance to travel, minimizing latency time. Embedding AI directly allows edge devices to respond to information in real time. This also eases the burden on wide-area networks to process excessive quantities of information. With most of the essential processing now taking place in the edge device, your network bandwidth is freed up to maintain your requirements.
In today’s market, rapid response time is a prerequisite to success. Perhaps even more important is the ability to be reliably responsive. When network connectivity is unreliable or spotty, devices are subject to lose their connection to the cloud. If this cloud connection is lost on a device that requires the cloud for its AI function, a faulty Wi-Fi connection can result in a nonfunctional device. While SOMs perform integral AI functions as edge computing powerhouses, your device can function without relying on an internet connection. Furthermore, SOMs deliver powerful processing capabilities and the storage to house large quantities of data as a cloud alternative.
It is hardly a secret that cloud based computing introduces security concerns. Your data will be stored in a cloud, often alongside unrelated companies or even those with sinister intent. Transmitting information through the cloud invites the potential for hackers to utilize packet sniffers, man-in-the-middle attacks, or other black hat tactics to intercept and corrupt your information. These concerns are elevated in recent years as prominent cyber attacks make headlines, stoking fears of security.
Embedded technology bypasses many of these security concerns by allowing for processing, storage, and interpretation of data all on-device. If data collection and subsequent AI functions are managed by your embedded technology your device is freed from reliance on data transmission to and from the cloud. Furthermore, many SOMs act as embedded solutions with built-in secure elements to protect your device in the most strenuous of applications. OEMs in regulated industries like healthcare and aviation trust SOM providers with a robust security suite. Even less regulated industries face pressures to protect consumer data, which is only possible with the embedded security to make it possible.
Cloud AI services housed in massive server farms collect data from thousands of edge devices in their network and use this data to inform generalized protocols. But generalized protocols informed by massive quantities of data are not always the best approach. In many instances a custom AI that adapts to its individual user’s behavior can deliver a more personalized experience. Consider the rising trend of smart speakers and home assistants. A generalized acoustic model would struggle to adapt to speech impediments or regional language variants. By embedding AI functions at the point of data collection your device can differentiate generalized patterns from the behavior of its specific user. This unlocks the potential for unlimited AI specialization informed by its own specific users.
The specialized value of embedded AI has sparked an industry transformation to deliver embedded solutions that are equipped to fulfill AI functions. Not all processors are created equal. So embedded systems and SOM developers have rushed to deliver the solutions best configured for AI applications. SOM providers like Beacon EmbeddedWorks deliver the most powerful and compact modules technology has to offer to drive cutting-edge solutions. This specialization capacity serves to mitigate the challenges of vastly varying application environments. When your SOM performs embedded AI functions your solution can self-optimize on a device by device basis.
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