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.
One of the key metrics for process efficiency is overall equipment efficiency (OEE). This KPI is related to the availability, performance and quality of the production process. High OEE often means good revenue.
In the wake of landmark climate event, COP26, manufacturers are striving to align their activities with the global net zero targets. But the sector has a long way to go. It currently produces more than 16 gigatons of carbon dioxide (CO2) each year.
Swedish philosopher Nick Bostrom once said, "machine intelligence is the last invention that humanity will ever need to make". AI decision making, with real time communication and data analytics, has the ability to transform the way manufacturers understand machines.
The edge is an essential layer of the manufacturing technology stack. Machines on the factory floor collect vast amounts of raw data from various sources using numerous protocols, which all needs to be processed quickly to gain actionable insight.
Manufacturers have become increasingly reliant on cloud-based resources, such as Software-as-a-Service (SaaS) applications and data migrating from the data center to multi-cloud environments. This requires a new model for secure network access.
To cope with increasing amount of data, edge computing hardware is being deployed to alleviate the burdens placed on the cloud and data centers. So, what are the computer hardware needs for edge computing? We will answer this question in much detail.
During POC projects in the last few years, many organizations have confirmed the benefits that IIoT can bring to a wide variety of industries - and IoT spending is expected to reach $1.1 trillion by 2025, according to IDC.
Edge computing serves two main purposes: extracting signal from noise by locally processing large volumes of data that are not feasible to send across the internet and providing the ability to process specific things locally where and when latency is a concern.
Edge computing uses dedicated, on-premise resources at the shop-floor level, rather than the remote servers that cloud computing relies on. This provides a significant increase in the rate and amount of data that manufacturers can process in real-time.
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