AI in Edge Computing

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Introduction to AI and Edge Computing

The rapid rise of the applications powered by Artificial Intelligence raises the data center's technical requirements, which generates high costs. The cloud is not a valid alternative in many cases. In these cases, the best option may be edge computing, which can provide the necessary computing power and minimize service delivery latency. Therefore it is necessary to Bringing AI into Edge Computing.






What is Edge AI?

Edge AI is the combination of Edge computing and Artificial Intelligence. With Edge AI, AI algorithms are executed locally on a hardware device using the data collected from Edge computing.

As the data is collected and processed in real-time, it reduces power consumption as well as data costs since the device doesn't need to be connected to the internet at all times. Edge computing brings processing, computation, and data storage closer to where it is generated and collected instead of relying on moving it to a remote location such as a cloud.

Does Edge AI exist?

Yes, it does. There are some accessible real-time instances in which the involved algorithms are used to process the data right in your device instead of sending it to cloud for obtaining results-

  • iPhone registering and recognizing your face for unlocking the phone in milliseconds.
  • Google Maps are pushing alarms about bad traffic.
  • Autonomous vehicles to put emergency brakes if AI algorithms predict any collision.
  • A security camera must recognize intruders and react immediately.
  • If a sensor predicts an explosion in a chemical plant, the plant needs to be shut down immediately.

What are the Benefits of Edge AI?

Edge AI's benefits are speed and can detect the issues by integrating smart devices and functionality to deploy AI at the edge for insights.
  • Its flexibility enables smart devices to support different industries.
  • Edge AI also offers high safety and security level with enhanced security features, and edge AI-powered devices help minimize this risk.
  • No special understanding is mandatory to operate the Edge AI-empowered devices. The device automatically offers the necessary insights on the spot through rich graphical interfaces or consoles.
  • It reduces cost and latency times for an improved user experience. This facilitates the integration of technologies focused on the user's experience, where you can interact in real-time to make payments.

What are the Disadvantages of Edge AI?

  • Edge devices can need more hardware and software for optimum output and local storage requirements, and costs may rapidly escalate as they're spread over many local geographies.
  • Some critics argue that while edge computing is beneficial, it also lacks the computing power of a cloud-computing infrastructure.
  • When you depend on edge devices, you get a bit more variety of machine styles. As a result, failure is more common.

Drivers of Edge Computing and Edge AI

Edge computing is a distributed computing model that does necessary computations and stores data closer to the location of the device. There is a misunderstanding that edge computing will replace Cloud computing. On the converse, it functions in association with the Cloud. Big data will always be processed on the Cloud. But, instantaneous data produced by the users and associates only to the users can be computed and processed on edge. There are numerous drivers of Edge Computing and Edge AI.

  • Latency - The apparent reason for tasks to be done on edge is latency. The delay while moving data to the Cloud for processing and then results are transmitting back over the network to a local device. In some situations, AI models must be processed at the edge or at the device itself so that decisions can be made faster without relying on network connectivity and moving extensive data back and forth over a network.
  • Privacy - In some scenarios, the sharing of personal and sensitive data (e.g. Finance sector) across boundaries has raised concerns regarding data privacy. Here, AI on edge helps by only sharing the data that requires further evaluation, which decreases the amount of data transferred and reduces the probability of breach in privacy.
  • Performance - AI models can process the data much quicker on the device itself as compared to the Cloud as the data does not need to travel back and forth. But there are still events where data processing at the Cloud is better. When judgments require extensive computational power and do not need to be executed in real-time, AI should stay in the Cloud.  For example, In healthcare, when AI is used to interpret an ECG or to analyze crop quality (in agriculture), data collected by a drone over a farm where one can wait a few minutes or a few hours for the decision, it is better to do this processing in the Cloud. 
  • Bandwidth - To generate insights from AI, data needs to move to the Cloud. As connection speed differs in various parts of the world and sometimes it is not easy to transfer data from/to the server from such remote locations. On the other hand, AI at the edge solves the problem by sending only the part of the required data for further analysis.
Source:
https://www.xenonstack.com/blog/ai-edge-computing


Present by:
Syed Abuthahir.T (21UCA047)
III BCA

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