AI at the Edge: Powerful Devices, Greater Storage

Artificial Intelligence (AI) is continuing its journey from being a mere pipe dream to a tool used by millions of businesses and consumers across the globe. This technology already has many, varied use cases, which have numerous benefits including improving productivity, enhancing analytics and automating some manual tasks. 

AI can be generated at the edge, in the cloud or at data centers. However, technology advancements and innovations will likely embed or put AI directly onto more end devices, like phones, tablets, laptops and sensors. With this will come numerous challenges that businesses must be aware of and address accordingly.

As these technologies come together, they will impact the architecture at the edge, and what is required from data storage. More specifically, they will drive a demand for specialized storage.

Understanding Embedded AI

While embedded AI technology on devices at the edge is relatively new, it is becoming increasingly common and will likely grow rapidly. It’s an exciting concept, as by transitioning away from the network core, embedded AI applications will become even faster and more useful, further enhancing AI’s use cases and capabilities.

Rather than just being a tool individuals can leverage on their devices, embedded AI systems will be able to better interact with the environment, analyze data in real time, make intelligent decisions and perform complex tasks without connectivity or cloud-based computing.

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By switching to AI-embedded devices, response times will be shorter, while safety and operational efficiency can be improved. This will be transformational in health care, manufacturing, transport and entertainment. The functions of an AI-powered device can be performed almost instantaneously rather than having a delay by performing computing in the cloud or data center.

Embedded AI in Action

In smart vehicles, embedded AI is being used to detect congestion and obstacles in the road. For self-driving cars, these insights are used to keep passengers safe without input from a human driver. By speeding up the reaction of the AI-enabled vehicle, the automatic braking system is more likely to be approved for road use as it will likely be more responsive to potential hazards on the road.

When used in healthcare devices, embedded AI can hugely impact the functionality of a healthcare sensor. Because of the increased speed associated with in-device AI computing, a device can take immediate action to provide required assistance for the patient as opposed to just providing an alert. In cases where patients have extreme allergies, for example, embedded AI insights can be used to automatically administer necessary medication to the patient. In time-sensitive situations, this could even be lifesaving.

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Beyond business use cases, AI at the edge brings a wide range of benefits to consumers leveraging the technology. In the home, embedded AI systems personalize Internet of Things (IoT) and smart home systems to ensure that settings such as lighting and temperature meet individual requirements taking factors such as time of day and outside weather conditions into account. Using embedded AI technology, homes are easier to manage and run more efficiently for residents.

For home security applications, edge AI is used to process data from surveillance systems, sending an instant alert if any unusual activity is detected. Using data to recognize residents’ and regular visitors’ faces, the embedded AI in the system records patterns of behavior. Therefore, when a new visitor comes onto the property outside of the regular pattern, the system alerts the homeowners. Using AI analytics, the system reduces the number of alerts from visiting family members and ensures that security systems are more efficient.

Power and Storage for Embedded AI

The switch to device-led AI will significantly increase the computing power that takes place in the device itself. Rather than having to rely on connectivity to the core of the network where AI processing takes place, devices themselves can facilitate the AI model.

In addition, these devices will require a bigger and faster storage capacity to allow for AI applications and to facilitate machine learning. As this storage is embedded, it will have to be physically small enough to fit into devices such as wearables or other portable technology. Therefore, the storage must be rugged and compact but massively powerful, with high performance and high capacity to cope with AI workloads.

As with any innovation that adds value, early adopters will likely see the first iterations of these devices in premium settings such as private healthcare or high-end vehicles. However, as compact solutions for embedded AI become more widespread, the use cases for embedded AI will expand to consumers and businesses of all sizes and industries.

The Next Step

There’s no doubt that AI-embedded devices will bring new and improved functionalities and experiences to end-users through better speed, productivity, safety and entertainment. Although the technology is largely still in the development phase, development is likely to be rapid due to its many potential benefits.

As this technology gains traction, we will likely see vendors integrating storage and AI computing functions into smaller, sleeker devices. As a consequence, these devices will help enable the next stage of AI development, as computing continues to move from the core to the edge.