What Is Edge AI

Innovative capabilities at the edge, facilitated by advancements in computing performance and efficiency, are bringing together the physical and digital worlds. Edge AI, which brings AI to local devices and sensors, enables rapid data analysis and action independent of the cloud or data center. This unlocks near-real-time responsiveness and insights, increased efficiency, reduced operational costs, and the ability to deliver new types of customer experiences.

Role of AI at the Edge

Whether the end goal is automating medical imaging workflows to accelerate diagnosis or improving operational efficiency at a metal fabrication plant, AI’s role at the edge is the same—to leverage data to take action faster. Within the realm of AI, this is achieved through a task known as inference.

During inference, live input data is fed into an AI model that has been previously trained to carry out functions such as making decisions, sending alerts, providing insights, or completing tasks such as sorting images.

Edge AI frequently uses Internet of Things (IoT) devices, including cameras, data sources, and sensors, to collect and analyze data out in the real world. Say an energy or utility company wants to keep their towers, pipelines, or grids safe from bad actors. Security video and sensor data can be processed at the edge, automatically alerting operators to threats in near-real time. With the ability to collect and process data nearly instantaneously, edge AI vastly expands an organization’s capabilities, enabling an array of AI-based applications and experiences at the edge. From a chatbot running on a bank kiosk, to cars with automated lane assist and collision avoidance capabilities, to near-real-time alerting of safety hazards or incidents on city streets or the plant floor, the use cases for edge AI across industries are quite expansive.

Edge AI vs. Traditional AI

Traditionally, AI has primarily been cloud based, with data being sent to the data center, where it is processed and returned post analysis. Such cloud-centric compute infrastructure models are not ideal for time-sensitive enterprise processes and operations.

Many enterprise inference use cases benefit from data being analyzed very fast. They often require real-time data processing and have strict latency requirements. Quite often, they need to be implemented at locations with poor connectivity, making them vulnerable to delays and errors from data packet loss during transmission to and from the cloud.

To address these needs and challenges, edge AI performs data analysis at the data source, such as the plant floor, the hospital, or the storefront. Algorithms are deployed on-site, where the data is processed either in a central hub or directly on edge devices with built-in processors.

Hybrid Edge AI

These two approaches, edge AI and cloud AI, are not mutually exclusive. As AI at the edge advances, a hybrid edge approach that distributes inference workloads between the edge and the cloud is expected to become widely adopted. The idea is that lightweight, near-real-time insight at the edge can be bolstered by deeper context in the cloud.

By combining the speed and efficiency of the edge with scalable cloud-based resources, the hybrid edge will facilitate cutting-edge applications with diverse deployment environments and performance demands. A hybrid approach will also allow enterprises to aggregate input from multiple models into their processes.

While edge AI is often associated with computer vision, the edge AI landscape is rapidly expanding to include multi-model applications involving generative AI (GenAI), natural language (text to speech, chatbots), and robotics. These emerging use cases are expected to revolutionize industries. In manufacturing, generative AI-driven software could facilitate an agile and dynamic supply chain, while autonomous vehicles and smart infrastructure could help smart cities reduce strain on the environment and optimize the flow of traffic.

Benefits of Edge AI

Edge AI can help enterprises tackle any number of complex challenges to solve real-world problems. Some of the benefits of bringing AI to the edge include:
 

• Operational speed and efficiency: Crucial to innovation, AI-based automation at the edge enables near-real-time, autonomous operations, eliminating delays associated with cloud-based processing. Latency and network bottlenecks are minimized, boosting data transfer rates.
• Cost-effectiveness: The growing volume of data from sensors and devices makes edge computing more cost-effective than sending data to the cloud and back. Less bandwidth is consumed, and fewer cloud-based resources are needed, helping to reduce operational expenses.
• Energy conservation: Energy-efficient edge AI devices are designed to facilitate low-power computing and can be significantly more efficient than cloud-based processing. Meanwhile, networking hardware like routers and switches consume less power, as traffic to and from the data center is minimized.
• Security and data sovereignty: Keeping sensitive data at the edge helps to reduce security and privacy risks by ensuring local control, autonomy, and compliance with regulations.

Edge AI Considerations

Bringing AI to edge environments presents new challenges when compared to running AI in the public or private cloud, including:
 

• Adding AI to existing investments: Many edge environments feature legacy, fixed-function infrastructure with a variety of proprietary equipment and software. Proprietary technologies with incompatible formats may present technical challenges when integrating them with an edge solution.
• Training and fine-tuning models: Edge AI models are unique and must be tuned for a specific industry or use case. Human domain knowledge is often critical in these cases. Enterprises need simple tools that help non‒data scientist experts transform their expertise into AI capabilities.
• Addressing hardware diversity: Edge-native applications will likely span a multitude of nodes, operating systems, connectivity protocols, compute and storage needs, energy and cost constraints, and compliance concerns. Developers need ways to deal with this complexity and support a distributed heterogeneous computing environment.
• Securing and managing distributed applications: Enterprises face new challenges as they seek to support advanced AI at the edge. Manageability is critical to applying AI at scale, and security is a necessity at every step along the way.
• Planning for rugged or constrained conditions: Edge environments place different kinds of stress on AI hardware, such as heat, moisture, or vibration. Edge AI solutions for use cases like traffic monitoring or quality assurance often need to be placed in areas with small amounts of physical real estate. Making it all happen with low power usage is also important for controlling costs and promoting sustainability.

Edge AI Solutions

How to modernize business operations with edge AI technology is a deep and multifaceted topic. Some organizations, such as those in manufacturing and industrial, will be looking to add edge technology and “intelligence” to legacy operational equipment as they evolve into a digital enterprise that uses near-real-time data to deliver value. Others, such as those in the financial and healthcare industries, have large, data-centric operations that are too vast or disparate for human monitoring and analysis. These organizations are moving toward digitizing and automating their data processes to uncover patterns and insights more quickly and to improve efficiency, compliance, and data security.

Regardless of which challenges an organization is trying to address, tackling AI enablement in phases is recommended. While the most advanced and wide-spanning use cases will require an AI tech stack of edge-to-cloud technologies, getting started with edge AI can be done without a major infrastructure investment. Businesses with current edge environments are likely ready to get started with AI today. Existing edge compute resources—like point of sale (POS) systems, industrial PCs, and local servers in healthcare offices—can support many AI workloads, including computer vision.

Integration Considerations

The main challenge to implementing an edge AI solution is overcoming the inherent complexity involved in coordinating the various pieces that make up the solution, including compute infrastructure, IoT devices, and legacy equipment. A unified technology platform can help reduce this complexity and promote interoperability between multiple AI environments and standardization across heterogeneous infrastructure, creating a unified fabric from edge to cloud.

Hardware Considerations

Whether training models in the cloud, fine-tuning them, or deploying them at the edge, choosing the right AI hardware can help businesses right-size their investment and support performance requirements.
AI processors support the entire AI pipeline—from massively complex model training to simpler AI needs, including incorporating AI in end user devices:
 

• Central processing units (CPUs) with built-in accelerator engines can help power many advanced edge AI workloads without the need for specialized hardware.
• GPU solutions can help power the most demanding workloads in the data center, at the edge, or in end user devices.
• FPGAs are often used as AI accelerators and AI processors to help enable AI workloads from edge to cloud. Compared to CPUs and GPUs, FPGAs are more versatile and can be reconfigured to suit a wide variety of use cases. FPGAs offer a combination of speed, programmability, and flexibility to deliver performance without the cost and complexity of developing custom chips.

Selecting an Adoption Approach

Organizations looking to extend AI applications to the edge generally fall into three categories: those looking to purchase a purpose-built AI solution or application, those looking to build their own AI application, and those looking to achieve their AI goals through some combination of the two approaches.

Building an AI solution: For those seeking to build their own edge AI solutions from the ground up, vendor-agnostic, edge-native software platforms can help with building, deploying, and iterating on AI workflows and feature the broadest interoperability and protocol support. Post-deployment edge-native platforms also make it simpler to manage and update AI software across all distributed edge environments.
When vetting platforms, it is important to look for:
 

• Support for the heterogeneous computing environments often found across the edge
• Open standards to help future-proof AI efforts
• Pro- and low-code development options
• The ability to import existing applications
• Integrated telemetry dashboards that can help right-size hardware and optimize applications
• Integrated security capabilities
• Cloud-like autoscaling and containerization features
   

Purchasing ready-made AI solutions: Enterprise organizations can also purchase AI solutions from a solution provider or system integrator. These vendors provide integrated hardware and software systems tailored to specific industry use cases and needs. It is important to choose a market-ready solution that has interoperable AI capabilities and a proven track record of successful industry and domain-specific deployments.

Taking a combined approach: Many organizations find that combining their own development efforts with prebuilt components can be the most efficient way to achieve AI success. A range of tutorials and resources demonstrating edge AI use cases are available and can help accelerate time to value and simplify development. Additionally, there is a wide range of software resources that can help ease the development lift, including frameworks, toolkits, industry-specific development tools, reference architectures, and reference implementations.