Edge computing makes it possible to process data on the device instead of sending it to the cloud. Beyond the security benefits it offers, this option overcomes the latency associated with moving information. As artificial intelligence (AI) becomes more prominent across a wide range of industries, more people are interested in combining the two technologies for mutual benefit to achieve edge AI computing goals. Many are exploring how to design edge AI and making careful adjustments to reach the optimizations they want. How can you follow their lead?
Take an all-encompassing design approach
Building an edge device to process AI content requires evaluating every aspect of the design, from hardware and software to power. Many AI processing tasks are already resource-intensive, so anyone who wants to create AI-friendly edge devices must apply forward-looking decision-making to overcome the known challenges.
From a hardware perspective, edge devices should have dedicated AI chips that provide the necessary processing capabilities. Then, as people review how the device's software will function, they should scrutinize every proposed feature to determine which ones are essential. This is a practical way to preserve battery life and ensure the device can maximize its resources for processing AI data.
Instead of taking a trial-and-error approach, people should strongly consider relying on industrial digital twins. These allow designers to preview the impact of their decisions before making them. Collaborative project management tools let leaders assign tasks to specific parties and encourage a culture of accountability. Comment threads are equally useful for seeing when and why individual changes occurred. It then becomes simpler and more efficient to return to other iterations when needed.
Get familiar with the Tiny AI movement
Knowing how to design edge AI means understanding that some improvements happen outside the device itself. One popular movement is Tiny AI, which integrates algorithms into specialized hardware to improve latency and conserve power consumption.
People advancing Tiny AI efforts typically take one or more of several approaches. Sometimes the goal is to shorten algorithms so as to minimize the computing power required to process them. Another possibility is to build devices with small but optimized hardware that achieves energy-efficient results while still being able to work with the most complex algorithms. Finally, people consider new ways of training machine learning algorithms that require less energy.
Answering application-specific questions — such as what kinds of AI data will be processed on the edge device, or how much information is associated with a particular use case — helps product designers determine which Tiny AI goals are the most valuable.
Create a list of essential characteristics and capabilities
An essential optimization in edge AI computing involves determining the device's critical performance attributes. Builders can then identify the steps needed to achieve those outcomes. One practical place to start is to consider how particular materials can provide desirable properties. Silicon and silicon carbide are two popular semiconductor materials that may come up in discussions about an edge device's internal components. Silicon carbide has become a popular option for high-performance applications thanks to its tolerance of higher voltages and temperatures.
Knowing how to design edge AI means the responsible parties must consider data storage details and built-in security measures. Because many users rely on AI to process information about everything from customer purchases to process improvement results, protecting sensitive data from cybercriminals is critical. A basic step is to encrypt all data. However, device-level administrator controls are also important for restricting which parties can interact with the information and how.
What steps must users go through to update or configure an edge device? Making the product as user-friendly as possible enables users to set up and update their devices — an important security-related step.
It is also important to keep future design requirements in mind. How likely is it that the business will be processing more information, or different types of information, within the next few years? Do developers plan to create and implement additional algorithms that could increase processing demands?
Keep learning about related efforts to combine edge computing and AI
Estimates suggest that by 2025, three-quarters of enterprise data generation and processing will occur outside the traditional cloud. This finding shows how important it is for experts to keep exploring how to create special-purpose edge computing devices capable of processing large volumes of data, including AI.
Some companies and customers may have specific requests for design teams, engineers, and others to follow, but it is also worthwhile to keep pace with events and innovations across the broader industry. Collaboration among skilled, knowledgeable parties can accelerate the pace of progress faster than when people work independently without exchanging ideas.
One example is a European Union-funded project called EdgeAI. It involves the coordinated activities of 48 research and development organizations across Europe. The three-year project will focus on edge computing and the intelligent processing required to handle AI applications on those devices.
Participants will develop hardware and software frameworks, electronic components, and systems while concentrating on edge AI computing. The long-term goal is for Europe to become a leading region in intelligent edge computing applications.
Those involved will demonstrate the potential across the board using the solutions they develop for real-world applications. These efforts will help show leaders how edge AI can bring them closer to their goals.
Record the details of how you design for edge AI
In addition to considering these actionable strategies, be sure to document your process carefully, including detailed notes on your rationale and results. Beyond helping you pass knowledge along to colleagues and others interested in the topic, keeping records lets you refer back to what you learned, opening the way to apply those details to new projects.
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