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Enabling Edge AI Inference with Compact Industrial Systems


Executive Summary

Artificial intelligence (AI) technology breaks away from static rule-based programming, substituting inference systems using dynamic learning for smarter decisions. Advanced AI technology combined with IoT technology is now redefining entire industries with smart applications.

An important trend across these industries is the shift of AI inference systems toward the edge, closer to sensors and control elements, reducing latency and improving response. Demand for edge AI hardware of all types, from wearables to embedded systems, is growing fast. One estimate sees unit growth at 20.3% CAGR through 2026, reaching over 2.2 billion units.

The big challenge for edge AI inference platforms is feeding high bandwidth data and making decisions in real-time, using limited space and power for AI and control algorithms. Next, we see how three powerful AI application development pillars from NVIDIA are helping Advantech make edge AI inference solutions a reality.

Edge AI inference supports what is happening today in an application– and looks ahead months and years into the future as it continues learning.

What is AI Inference

There are two types of AI-enabled systems: those for training,and those for inference. Training systems examine data sets and outcomes, looking to create a decision-making algorithm. For large data sets, training systems have the luxury of scaling, using servers, cloud computing resources, or in extreme cases supercomputers.They also can afford days or weeks to analyze data.

The algorithm discovered in training is handed off to an AI inference system for use with real-world, real-time data. While less compute intensive than training, inference requires efficient AI acceleration to handle decisions quickly, keeping pace with incoming data. One popular option for acceleration is to use GPU cores, thanks to familiar programming tools, high performance, and a strong ecosystem.

Traditionally, AI inference systems have been created on server-class platforms by adding a GPU card in a PCIe expansion slot. Most AI inference still happens on AI-enabled servers or cloud computers, and some applications demand server-class platforms for AI acceleration performance. Where latency and response are concerns, lower power embedded systems can scale AI inference to the edge.

Advantages of Edge AI Inference Architecture

Edge computing offers a big advantage in distributed architectures handling volumes of real-time data. Moving all that data into the cloud or a server for analysis creates networking and storage challenges,impacting both bandwidth and latency. Localized processing closer to data sources, such as preprocessing with AI, can reduce these bottlenecks, lowering networking and storage costs.

There are other edge computing benefits. Personally identifiable information can be anonymized, improving privacy. Security zones reduce chances of a system-wide breach. Local algorithms enforce real-time determinism, keeping systems under control, and many false alarms or triggers can be eliminated early in the workflow.

Extending edge computing with AI inference adds more benefits. Edge AI inference applications scale efficiently by adding smaller platforms. Any improvements gained by inference on one edge node can be uploaded and deployed across an entire system of nodes.

If an edge AI inference platform can accelerate the full application stack, with data ingestion, inference, localized control, connectivity,and more, it creates compelling possibilities for system architects.

For more content, please download the white paper

  • Flexibility of CPU+GPU Engines for the Edge
  • Pillar 1:  Scalable System-on-Modules for Edge AI
  • Pillar 2: SDK for Edge AI Inference Applications
  • Pillar 3: Ecosystem Add-Ons for Complete Solutions
  • Systems for Mission-Critical Edge AI Inference