Edge Computing and AI: Exploring the Benefits, Applications, and Use Cases

Edge AI combines two of the hottest trends in the data center and IT industries: edge computing and artificial intelligence. As we defined in previous posts, there are many types of edge computing applications and environments, but at its core, edge computing pushes processing power out to the network edge, closer to users and data. This allows data to be processed at its source rather than in a distant data center or the cloud. By reducing the distance data must travel, edge computing improves the user experience and enables organizations to leverage data for real-time decision-making.

AI, of course, refers to algorithms that mimic the human mind. An AI-enabled system can “learn” from data and perform tasks it’s not explicitly programmed to do. AI has taken off in recent years due to advancements in GPU processors and neural networks, as well as the availability of vast cloud resources.

Let’s explore how edge computing and AI come together in a concept known as edge AI.

What Is Edge AI?

Deep neural networks make AI systems “intelligent.” They are made up of computational models with multiple processing layers and trained using vast amounts of data. The neural network analyzes the data to find patterns so it can answer the types of questions it will be presented with. Human trainers analyze the results and finetune the model based on the accuracy of the answers. The successful model becomes the “inference engine” capable of answering real-world questions.

Improvements in processing power make it possible to move the inference engine to the network’s edge, closer to the data it must analyze. Data stays in smaller edge data centers and is only transferred to a centralized data center or the cloud when the inference engine needs additional training. 

Smaller, less expensive chips give edge devices the power needed to process data for AI. Edge AI chips also produce less heat and consume less energy, conserving device battery life. 

Edge AI Benefits and Use Cases

Edge AI reduces network bandwidth requirements, improves security, and, most importantly, minimizes latency. Reduced latency is what makes AI chatbots useful. Users can ask questions and get answers in seconds, not hours. Reduced latency also enables smart devices to make decisions in milliseconds, allowing organizations to leverage the full power of AI to drive autonomous systems. AI can be embedded in all kinds of devices, from manufacturing equipment to medical devices to transportation infrastructure. The marriage of edge computing and IoT devices enables automation at scale across various industries and applications. In fact, AI is one of the main drivers of the Fourth Industrial Revolution (Industry 4.0). 

Self-driving vehicles are a well-known application of edge AI. Following are just three of the many other use cases.

Manufacturing & Industrial Applications

Also known as industrial edge computing, enterprises across the globe have adopted edge computing in their manufacturing processes because of its numerous benefits. Smart sensors can detect problems in manufacturing equipment and alert operators if the equipment needs repair or preventive maintenance. This helps manufacturers minimize downtime by addressing problems quickly. Smart sensors can also detect issues during the manufacturing process so operators can reduce waste and avoid costly rework.

Healthcare Applications

AI systems can analyze data from health monitoring devices in real time, enabling clinicians to make better decisions about patient care. AI-enabled surgical instruments can detect subtle changes in the patient’s tissue and provide feedback that helps guide the surgeon’s movements.

Retail Applications

AI enables e-commerce companies to offer voice-enabled options on their website. Shoppers can use their mobile devices or smart speakers to search for products and place orders with voice commands, enhancing the customer experience.

Edge AI Market Size

Experts say the edge AI market has not grown as quickly as expected due to the long development times for AI-enabled devices and a lack of awareness of the edge AI space. The market is starting to take off, however.

According to a report from Fortune Business Insights, the edge AI market was valued at $15.6 billion in 2022. Grand View Research expects the market to see a compound annual growth rate of 21 percent through 2030. The expansion of 5G networks and the growing use of Internet of Things (IoT and IIoT) devices are helping to enable this growth.

Edge AI Starts with the Right Server Cabinets

Edge AI requires a significant amount of computing power at remote locations. To ensure the performance, availability, and security of these systems, you need server cabinets with built-in cooling, monitoring, and power protection. Enconnex offers a variety of self-contained micro data center cabinets built to enable edge computing in non-traditional environments. The Enconnex EdgeRack 3P delivers up to 3.5kW of cooling capacity, while the EdgeRack Industrial 8M provides 8kW of cooling capacity and dust and moisture resistance. The Enconnex data center infrastructure specialists are here to help you identify the right solutions for your edge AI deployments.