How to Optimize Edge Devices for AI Processing

Edge computing allows data to be processed on devices rather than transferred to the cloud. Besides offering security-related benefits, this option can overcome the latency associated with moving information. As artificial intelligence (AI) has become more prominent in various industries, more people are interested in meeting edge AI computing goals by combining the two technologies for mutual benefits. Many are also exploring how to design for edge AI, making careful tweaks that result in the desired optimization. How can you follow their lead? 

Take an All-Encompassing Design Approach 

Creating edge devices to process AI content requires evaluating all design aspects, from hardware and software to power sources. Many artificial intelligence processing tasks are already resource-intensive, so those who want to make AI-friendly edge devices must apply forward-thinking decision-making to overcome known challenges. 

From a hardware perspective, edge devices should have dedicated AI chips that offer the necessary processing capabilities. Then, as people review how the device’s software will function, they should heavily scrutinize all proposed features to determine which are essential. That is a practical way to conserve battery life and ensure the device can maximize the resources for handling AI data. 

Rather than using trial-and-error approaches, people should strongly consider relying on industrial digital twins, which enable designers to see the likely impacts of decisions before committing to them. Collaborative project management tools allow leaders to assign tasks to specific parties and encourage an accountability culture. Comment threads are similarly useful for determining when individual changes occurred and why. Then, reverting to another iteration when necessary is more straightforward and efficient. 

Become Familiar With the Tiny AI Movement

Knowing how to design for edge AI means understanding that some enhancements occur outside the devices themselves. One popular movement is Tiny AI, which integrates algorithms into specialized hardware to improve latency and conserve power consumption. 

Those furthering Tiny AI efforts generally take at least one of several approaches. Sometimes, they aim to shorten the algorithms, minimizing the computational capabilities required to handle them. Another possibility is to build devices with small but optimized hardware that can continue working with the most complex algorithms while getting energy-efficient results. Finally, people consider new ways of training machine learning algorithms that require less energy.

Answering application-specific questions, such as the kind of AI data processed by the edge device or the amount of information associated with the particular use case, will help product designers determine which Tiny AI aim is most valuable.

Create a List of Must-Have Characteristics and Capabilities 

An essential optimization in edge AI computing involves determining the device’s crucial performance attributes. Then, creators can identify the steps to achieving those outcomes. One practical way to start is to consider how specific materials may have 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 due to its tolerance to higher voltages and temperatures. 

Knowing how to design for edge AI also requires the responsible parties to consider data storage specifics and built-in security measures. Since many users rely on AI to process information about everything from customer purchases to process improvement results, it’s critical to protect sensitive data from cybercriminals. A fundamental 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 take to update or configure their edge device? Making the product as user-friendly as possible will enable people to set up and update their devices — a critical security-related step. 

It’s also important to keep future design needs in mind. How likely is it that the business will process more or a different type of information within the next several years? Do its developers intend to create and implement additional algorithms that could increase processing needs? 

Stay Aware of Relevant Efforts to Pair Edge Computing and AI

Estimates suggest that three-quarters of enterprise data creation and processing will happen outside the traditional cloud by 2025. That finding drives home how important it is for professionals to keep exploring how to make purpose-built edge computing devices that can handle large quantities of data — including AI. 

Although some companies and clients will have specific requests that design teams, engineers, and others should follow, it is also valuable to stay abreast of events and innovations in the wider industry. Collaboration between skilled, knowledgeable parties can speed up progress faster than when people work independently without bouncing ideas off each other. 

One example is a European Union-funded project called EdgeAI. It involves coordinated activities from 48 research and development organizations within Europe. The three-year project will center 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, all while remaining focused on edge AI computing. The long-term goal is for Europe to become a leading region in intelligent edge computing applications. 

Those involved will use the solutions they have developed for real-life applications, demonstrating their board potential. Such efforts will be instrumental in showing leaders how edge AI can get them closer to goals.

Record Details for How to Design for Edge AI

Beyond considering these actionable strategies, you should also carefully document your processes and include detailed notes about your rationale and results. Besides assisting knowledge transfer to your colleagues and others interested in this topic, your records will allow you to refer to what you have learned, paving the way for applying those details to new projects.