What Is AI on Edge Networks?

Rapid technological advances have made artificial intelligence (AI) one of the most promising and important technologies of the 21st century. As the computing demands of AI continue to grow at an exponential pace, many AI workflows are now moving from cloud environments to edge networks, allowing computation to happen closer to where data is collected, instead of in distant, centralized cloud data centers. This proximity enables AI systems to dramatically improve processing speeds while making AI data more secure. The move from the cloud to the edge is likely to play a critical role in enabling AI to achieve exciting and bold new capabilities.

Edge networks are an IT architecture in which computer functions like processing, data storage, networking, and security take place at the edge of the network, closer to the users, devices, and applications that generate and consume data. This model offers a contrast to cloud computing, which provides on-demand access to virtualized computing resources over the internet, from data centers that may reside anywhere in the world. By processing, storing, and serving data from locations that are closer to where data is generated, edge networks help to minimize latency, improve security, increase availability, and enhance user experiences.

In the early days of AI, most computing functions were performed in cloud-based data centers that offered the enormous computing power, capacity, and scalability required for this burgeoning technology. As the technology matures and data collection sources expand to the Internet of Things (IoT) and to billions of smartphones and connected devices, the resulting massive amount of data has increased latency and network bandwidth usage in cloud-based AI operations. As a result, AI technologies are now moving to the edge, processing datasets much nearer to the devices, applications, and users where data is generated. Running AI on edge servers enables emerging technologies like machine learning, neural network acceleration, and deep learning to scale more easily and provide real-time insights and analytics to drive smarter and faster decision-making.

Running AI on edge networks flips the traditional framework for artificial intelligence systems. In the past, data generated by AI-related sources was sent to a central cloud computing environment where massive amounts of compute power could aggregate, analyze, interpret, and communicate information about the data collected from AI sources. Today, rather than running centrally, AI models are configured on processors attached to or inside the devices at the network edge, including IoT devices, cell phones, autonomous vehicles, smart watches, smart appliances, and medical devices in addition to standard edge devices like laptops, PCs, and physical servers.

Edge AI applications offer tremendous advantages for this rapidly evolving field.

• Lower latency. Processing AI workloads on edge devices minimizes or eliminates the delays that occur when data must travel to and from distant cloud servers.
• Increased reliability. Edge AI operations can continue to operate even when central data centers go offline. Edge networks are quite adept at routing traffic around bottlenecks and network congestion to maintain reliability and availability.
• Greater privacy and security. By processing data closer to where it’s generated, edge networks reduce the risk that sensitive data will be exposed on the public internet or intercepted by cybercriminals as it’s transmitted to distant cloud centers.
• Reduction in bandwidth. Managing AI data locally reduces bandwidth consumption on networks, increasing overall network performance and availability.
• Cost savings. By using myriad edge devices rather than a central cloud database for CPU, GPU, and memory, edge AI operations can significantly reduce the cost of bandwidth for transmitting data.

AI on edge networks is driving new capabilities in virtually every industry.

• Energy. Edge AI models use real-time data related to weather patterns, energy consumption, and the health of the grid to generate and deliver energy more efficiently.
• Healthcare. Smart medical instruments rely on edge AI technology to scan medical images, assist in surgery, and communicate vital information about patient health to clinicians.
• Security. Facial recognition, object detection, and computer vision are dramatically improving security across many industries. In many applications, these technologies require ultra-low latency provided by edge AI processing to make timely security decisions.
• Manufacturing. Industrial Internet of Things (IIoT) networks rely on AI algorithms to monitor production quality and workplace safety, and make real-time adjustments to production processes.
• Speech recognition. The ability to run AI on edge devices is essential to enabling the algorithms that power speech recognition on cell phones and virtual assistants.