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  •  01/16/2023

Get a head start for AI at the edge

"ChatGPT" is referred as the hottest technology topic in recent months. AI simplifies and lessens the load on humans in all technological areas, enabling them to make more informed decisions. Several different tools and strategies are used to enhance technology and save time. AI may employ storage to feed machine learning while optimizing it for high-demand tasks. These systems can better react quickly to challenges that arise in the present and to requests for system growth.

The term "storage server" refers to a particular server used to secure and manage digital data, files, and services. It is a server explicitly created for storing and accessing enormous amounts of data across a public network or the Internet. A data server is a piece of software or a platform that offers database services such as data processing, storage, and security. Other software components or programs use these database services. Data storage is the term for magnetic, optical, or physical media that captures and keeps digital information for present or future use.

What exactly does "the edge" mean?

Even if the data is ultimately delivered to a data centre, an edge application is any distributed application in which specific processing takes place away from the server. The central concept is to forgo transmitting all the data over the Internet to be analyzed on a server and instead let information be studied close to where it is received, eliminating latency difficulties with long data roundtrips and enabling near real-time reaction.

About Edge AI 
  • It should be no surprise that Edge AI combines edge computing with Artificial Intelligence. The embedded systems known as edge devices or edge computing connect our digital and physical worlds. They frequently have sensors that provide them with data about the surroundings they are near. 
  • The machine learning (ML) techniques used in Edge AI systems can be run on current CPUs or even less powerful microcontrollers (MCUs) in edge devices. Edge AI offers higher performance while also consuming less power than other applications that use highly effective AI devices. Meanwhile, Edge computing is the direct processing of data and information at the IoT unit or device or the edge. Because of its intrinsic benefits, such as real-time analytics, reduced latency, high speed, etc., edge computing is expanding quickly.
  • Edge computing is a networked computing model that relocates computation and data storage closer to the source. The data generated on the device, whether or not it has an internet connection, is processed by artificial intelligence algorithms. Because of this, data may be processed quickly, giving real-time feedback.
  • The edge is broadly divided based on how far the server is from the terminal or data source. Applications close to the data center, possibly even inside the same structure, can be included in the so-called near edge. In uses like driverless vehicles, the far edge goes to the opposite extreme. The edge system processes data that would typically be transmitted to a data centre, which is the feature that overlaps. 

What " Hardware and Software” can be used for Edge AI ?

Edge AI may operate on various hardware platforms, including basic MCUs and cutting-edge neural processing units (NPU). Many machine learning algorithms are combined into edge AI software, which is operated on actual hardware. Running AI algorithms on a local device is the concept. Since Edge AI software does not rely on other systems or internet connections to connect to others, users can access data in real-time. All these devices with edge AI connectivity employ embedded algorithms to track their behavior and gather and interpret their data. The devices will make decisions, automatically fix issues, and forecast performance in the future. 

Benefits of Edge AI 
  • Adaptability: Edge AI often handles plenty of data processing. Transferring the data to a cloud service is unnecessary if the need to process video picture data from numerous sources simultaneously.
  • Lesser delay: Data transfer from the cloud requires time. Edge AI lowers delay by localizing data processing.
  • Increased dependability: The Edge AI System is more reliable thanks to increased speed and security standards.

Faster, Smaller and Smarter than Traditional Central Computing

Our daily tasks and work are made easier thanks to the power of Edge AI. The rapid processing power of edge devices will power everything, including remote work and commutes to the office. Edge computing will improve emerging technologies and the immersive experiences they bring. Recently, USI helped customers launch state-of-the-art edge AI computing servers supporting scenarios like outdoor telecommunication boxes, computer rooms, factories, shops, and other environments.

This 2U rack-mount edge AI server is equipped with Intel's third-generation Xeon Platinum processors, four single-width or two double-width NVIDIA GPUs, a short body that fits into small spaces, and is quiet, rugged, dust-proof, and shock-proof. USI provided a comprehensive architecture and conceptual design, chassis design, hardware, FW design (Switch FW, BIOS, and BMC FW), and thermal design. The product complies with stricter requirements than standard rack servers NEBS Telecommunications Standard to ensure it runs smoothly in a different harsh working environment.  

Edge computing provides a more efficient alternative. Processing and analyzing data closer to where the data is significantly created reduces latency. There are better development prospects and promotional potential with faster response times than traditional central computing architectures. In the future, USI will continue developing the server storage market with efficient turnkey design, production, testing, and verification services.


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