What Is Networking for AI?

High-performance networking for AI is a critical technology component to support successful AI deployments. Before implementing or expanding AI operations, organizations should analyze their networking infrastructure and determine what enhancements will be needed to support new AI-based capabilities.

AI workloads typically require significant compute resources and near-instantaneous responsiveness. This can be accomplished only with support from reliable, robust, flexible, and scalable networking solutions. AI can be implemented anywhere, so network solutions may include wired, wireless, and virtualized connections between and among disparate systems and devices in the AI data center, client, cloud, and edge.

Security is also a key requirement of AI networking. Most AI applications draw on massive amounts of data, including customers’ personal information, corporate intellectual property, and proprietary algorithms, all of which must be protected during transmission.

AI Networking Supports High Performance

Conversely, AI can also accelerate and strengthen network infrastructure itself. AI-enabled network and telecommunications infrastructure can improve access to and performance of the applications that run on them, including AI workloads.

The end goal of AI in networking is to automate tasks across network domains so the network can operate more efficiently, even as network traffic and complexity continue to expand.

Role of Networking for AI

AI is being used to automate myriad tasks to improve efficiency, accuracy, and responsiveness across industries. Most of these AI use cases rely on high-performance networking.

As AI becomes nearly ubiquitous, consumers expect seamless performance from chatbots, recommendation engines, and in-store kiosks, among many AI-enabled use cases. Automotive applications, such as autonomous driving, require near-real-time responsiveness for human safety. AI applications that help automate countless processes in a wide variety of settings rely on network infrastructure to deliver the efficiency and business returns expected from the use of AI.

Interoperability and Network Security

AI applications are often distributed among data centers, the cloud, the client, and edge devices, so interoperability is an especially critical aspect of network performance. Robust network security capabilities are also essential to protect enormous datasets, including users’ personal information, that feed machine learning algorithms and other AI programs.

These network security capabilities must be extremely responsive and efficient because most AI applications cannot tolerate latency. Security features rely on compute-intensive encryption, decryption, compression, and decompression algorithms, which require significant acceleration to keep from adding intolerable lag time to the transmission of AI-based data and operations.

Benefits of Networking for AI

High-performance networks designed for AI must provide certain specific benefits for AI applications to operate efficiently, securely, and with required responsiveness. These benefits include high bandwidth, low latency, scalability, efficiency, and data security.

High Data Bandwidth

Many AI workloads access and transmit huge datasets, either consistently or sporadically. Networking designed for AI will provide high bandwidth to accommodate data traffic and maintain data integrity, especially in cloud-based applications.

Low Latency

AI networking use cases often depend on flawless response within milliseconds, especially at the edge. Networks built to support AI success must provide consistent, stable connectivity with extremely low latency.

Scalable Performance

AI applications often accumulate data over time, and the user base may grow too. Additionally, AI solutions may be distributed between and among disparate systems and devices, requiring the ability to accommodate many concurrent connections. Networks designed up front to support multiple use cases and future changes in scope and magnitude enable AI workloads to continue to scale dynamically without sacrificing performance.

Efficient Resource Utilization

Powerful AI networks must be optimized to ensure efficiency and prevent costly over- or underprovisioning of network and computing resources. A fully optimized networking infrastructure can help reduce expenses in the AI data center and the cloud.

Data Protection and Network Security

Network security is even more important for AI workloads than for many other applications because AI use cases depend on massive datasets that often include private, valuable, or highly regulated information. Networks that include advanced threat detection and prevention capabilities can help to minimize the risk of costly, disruptive data breaches.

AI Networking Solutions

Networking is a key enabling technology for AI use cases; however, networking needs for AI applications can vary widely depending on the scope of the application, the number and location of users, and the type of networking infrastructure. Additionally, network design becomes more complex as AI-enabled solutions are distributed across the data center, cloud, and edge.

To determine what networking solutions will be required, businesses considering adding AI capabilities should first analyze existing networking infrastructure and define key requirements for their identified AI use cases.

For example, many network-dependent AI use cases, including smart factory applications, transportation and event management, and public safety scenarios, rely on private 5G at the network edge.

Other AI solutions, such as recommendation engines, run in the cloud, data center, or both and rely on a combination of wired, wireless, virtual, and software-defined networks that can scale to serve thousands or even millions of remote users. These networking solutions are themselves compute intensive and rely on high-speed, high-bandwidth controllers, ports, interfaces, and data pipelines.

The explosive growth of AI-based automation is prompting developers and vendors to accelerate the time to market. Many turn to open networks and standards to find common building blocks and customizable solutions that can be deployed across multiple platforms.

Leading technology providers support these emerging requirements by working collaboratively to establish next-gen Ethernet technologies and standards that help ensure interoperability and compatibility across a range of vendors and products.