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Edge AI

(Washington D.C., U.S.A.)


Edge AI: The Future of AI and Edge Computing


- Micro-Data Centers

Edge computing plays a vital role in the efficient implementation of several embedded applications such as artificial intelligence (AI), machine learning, deep learning, and the Internet of Things (IoT). However, today's data centers are currently unable to meet the requirements of these types of applications. This is where the Edge -Micro Data Center (EMDC) comes into play.

By moving intelligence closer to the embedded system (i.e., the edge), it is possible to create systems with a high degree of autonomy and decision-making capabilities. In this way, reliance on the cloud (typically centralized systems) is reduced, resulting in benefits in terms of energy savings, reduced latency, and lower costs.

Self-driving cars, robotic surgery, augmented reality in manufacturing, and drones are a few examples of early applications of edge computing. As of today, current data centers with "cloud services" (hyperscale, mega, and colocation) cannot meet the requirements of these applications, thus requiring complementary edge infrastructure such as EMDC and "edge services".

This edge infrastructure, hardware, and edge services must meet the following requirements:

  • High computational speed, requiring data to be processed as locally as possible (i.e. at the edge)
  • high elasticity
  • high efficiency


- Edge AI

Edge AI means that AI software algorithms are processed locally on a hardware device. The algorithms are using data (sensor data or signals) that are created on the device. A device using Edge AI software does not need to be connected in order to work properly, it can process data and take decisions independently without a connection.

AI relies heavily on data transmission and computation of complex machine learning algorithms. Edge computing sets up a new age computing paradigm that moves AI and machine learning to where the data generation and computation actually take place: the network’s edge. The amalgamation of both edge computing and AI gave birth to a new frontier: Edge AI. 

Edge AI allows faster computing and insights, better data security, and efficient control over continuous operation. As a result, it can enhance the performance of AI-enabled applications and keep the operating costs down. Edge AI can also assist AI in overcoming the technological challenges associated with it. 

Edge AI facilitates machine learning, autonomous application of deep learning models, and advanced algorithms on the Internet of Things (IoT) devices itself, away from cloud services.

The University of Chicago_050723B
[The University of Chicago]

- Edge AI Is The Next Wave of AI

Edge AI is the next wave of artificial intelligence (AI). detaching the requirement of cloud systems. Edge AI is processing information closer to the users and devices that require it, rather than sending that data for processing in central locations in the cloud.

In the last few years, AI implementations in various companies have changed around the world. As more enterprise-wide efforts dominate, Cloud Computing became an essential component of the AI evolution. As customers spend more time on their devices, businesses increasingly realize the need to bring essential computation onto the device to serve more customers. This is the reason that the Edge Computing market will continue to accelerate in the next few years.


- The Enterprise Distributed Edge and Edge AI

Edge AI and edge computing devices help many industries become more efficient and safer by improving accuracy and reducing human error through automation. Edge AI can significantly improve surveillance and monitoring while reducing the amount of raw data that’s transmitted to the cloud leading to the adoption of edge AI for video surveillance. With the advent of edge AI, ML intelligent camera systems can capture raw data, process, and analyze it using facial recognition to identify persons of interest and suspicious activities that may be occurring directly at the edge. 

We now see industries across the board tapping the potential of these edge computing devices to improve day-to-day life for everyone. Early adopters of edge AI and edge computing technologies include industries such as transportation/driverless vehicles, education, medical/healthcare, agriculture, manufacturing/factories, retail/shopping and video surveillance.


- Edge Intelligence for Beyond 5G Networks

Beyond fifth-generation (B5G) networks, or so-called “6G”, is the next-generation wireless communications systems that will radically change how Society evolves. Edge intelligence is emerging as a new concept and has extremely high potential in addressing the new challenges in B5G networks by providing mobile edge computing and edge caching capabilities together with Artificial Intelligence (AI) to the proximity of end users.

In edge intelligence empowered B5G networks, edge resources are managed by AI systems for offering powerful computational processing and massive data acquisition locally at edge networks. AI helps to obtain efficient resource scheduling strategies in a complex environment with heterogeneous resources and a massive number of devices, while meeting the ultra-low latency and ultra-high reliability requirements of novel applications, e.g., self-driving cars, remote operation, intelligent transport systems, Industry 4.0, smart energy, e-health, and AR/ VR services.

By integrating AI functions into edge networks, radio networks become service-aware and resource-aware to have a full insight into the operating environment and can adapt resource allocation/orchestration in a dynamic manner. Despite the potential of edge intelligence, however, many challenges also need to be addressed in this new paradigm. Until now, limited research efforts have been made on edge intelligence for B5G networks.  



[More to come ...]

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