How AI is Reshaping the Edge Computing Landscape

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How can today's memory and storage technologies meet the stringent requirements of the challenging new edge applications?

www.eetasia.com, Nov. 17, 2022 – 

How much computing power is needed at the edge? How much memory and storage are enough for AI at the edge? Minimum requirements are growing as AI opens the door to innovative applications that need more and faster processing, storage, and memory.

How can today's memory and storage technologies meet the stringent requirements of these challenging new edge applications?

What do we mean by "the edge"?

Edge includes any distributed application where specific processing occurs away from the server, even if the data is eventually sent to a data center. The big idea is to avoid sending all the data over the internet for processing on a server and instead allow data to be processed closer to where it's collected, avoiding latency issues with long data roundtrips, and enabling near real-time response on site.

The edge is roughly divided according to the distance from the server to the endpoint. The so-called near edge can include applications close to the data center, perhaps even within the same building. The far edge takes the other extreme in applications such as autonomous vehicles. The overlapping feature is that the edge system processes data that would have traditionally been sent to a data center. This has practical applications in many industries.

Data latency and bandwidth at the industrial edge

In industrial applications, edge computers are typically designed to take inputs from sensors or other devices and act on the inputs accordingly. For example, preventative maintenance takes acoustic, vibration, temperature, or pressure sensor readings and analyzes them to identify anomalies that indicate slight faults in machines. Machines can be taken offline immediately or when needed to enable maintenance to occur ahead of catastrophic failure. Reaction times must be quick, but data quantity is low.

However, AI is putting a strain on these edge systems.

The impact of AI on edge processing loads

AI places a different kind of load on computer systems. AI workloads require faster processors, more memory, and powerful GPUs. AOI, for example, has seen widespread adoption for PCB inspection, using video input from high-speed cameras to identify missing components and quality defects. In fact, similar visual inspection technology is seeing adoption in industries as diverse as agriculture where it can be used to identify defects and discoloration in produce.

Performing complex algorithms on video inputs requires the parallel processing capabilities of power-hungry GPU cards, more memory for efficient and accurate AI inference, and more storage space for additional data. But don't these already exist in data centers?

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