With the explosive growth of mobile computing and Internet of Things (IoT) applications, billions of mobile and IoT devices are being connected to the Internet, generating massive amounts of data at the network edge. The collection of massive volumes of data in cloud data centers incurs extremely high latency and network bandwidth usage.
Therefore, there is an urgent need to push the frontiers of artificial intelligence (AI) to the network edge to fully unleash the potential of big data. Edge AI is the combination of edge computing and AI.
In this article, we will cover the following topics:
- What is Edge AI?
- Why we need Edge AI
- Applications of Edge AI
What is Edge AI?
- Big Data. Today, in the era of the Internet of Things (IoT), an unprecedented volume of data generated by connected devices needs to be collected and analyzed. This leads to the generation of large quantities of data in real-time, which requires AI systems to make sense of data.
- AI in the Cloud. Initially, AI solutions were cloud-driven due to the need for high-end hardware capable of performing deep learning computing tasks and the ability to effortlessly scale the resources in the cloud. This involves offloading data to external computing systems (Cloud) for further processing, but this worsens latency, leads to increased communication costs, and drives privacy concerns.
- Edge Computing. To address those issues, there is a need to move the computing tasks to the edge of the network, closer to where the data is generated. Edge Computing refers to computations being performed as close to data sources as possible, instead of on far-off, remote locations. Hence, edge computing is used to extend the cloud as it is typically implemented in the form of edge-cloud systems, where decentralized edge nodes send processed data to the cloud.
- Edge AI. Edge AI, or Edge Intelligence, is the combination of edge computing and AI, it runs AI algorithms processing data locally on hardware devices (on-device AI). Therefore, Edge AI takes the advantages of rapid response with low latency, high privacy, more robustness, and better efficient use of network bandwidth. The use of Edge AI is driven by emerging technologies such as machine learning, neural network acceleration, and reduction.
Advantages of Edge AI
Edge computing enables bringing AI processing tasks from the cloud to near the end devices in order to overcome the intrinsic problems of the traditional cloud, such as high latency and the lack of security.
Hence, moving AI computations to the network edge has several advantages:
- Lower data transfer volume. Data is processed by the edge device and only a significantly lower amount of processed data is sent to the cloud. By reducing the traffic amount across the connection between a small cell and the core network, the bandwidth of the connection can be increased to prevent bottlenecks, and the traffic amount in the core network is reduced.
- Speed for Real-time computing. Real-time processing is a fundamental advantage of Edge Computing. The physical proximity of edge devices to the data sources makes it possible to achieve lower latency which improves real-time data processing performance. It supports delay-sensitive applications and services such as remote surgery, tactile internet, unmanned vehicles, and vehicle accident prevention. A diverse range of services, including decision support, decision-making, and data analysis, can be provided by edge servers in a real-time manner.
- Privacy and security. Keeping data at the edge is private, as transferring user data over networks makes it vulnerable to theft and distortion. Edge computing allows to ensure that private data never leaves the local device. For the cases where data must be processed remotely, edge devices can be used to discard personally identifiable information prior to data transfer, thus enhancing user privacy and security.
- High availability. Decentralization and offline capabilities make Edge AI more robust by providing transient services during a network failure or cyber-attacks. Therefore, deploying AI tasks to the edge ensures significantly higher availability and overall robustness needed for mission-critical or production-grade AI applications (on-device AI).
- Cost advantage. Moving AI processing to the edge is highly cost-cost efficient because only processed, highly valuable data is sent to the cloud. While sending and storing huge amounts of data is still very expensive, small devices at the edge have become more computationally powerful – in accordance with Moore’s Law.
Edge AI and 5G
The urgent need for or 5G in high-growth areas like fully self-driving cars, real-time virtual reality experiences, and mission-critical applications further drive innovation around edge computing and Edge AI.
5G is the next-generation cellular network that aspires to achieve substantial improvement on the quality of service, such as higher throughput and lower latency – offering 10x faster data rates than existing 5G networks.
To understand the need for fast data transmission and local on-device computing, consider real-time packet delivery among self-driving cars that requires an end-to-end delay of less than 10 ms. The minimum end-to-end delay for access to the cloud is greater than 80 ms, which is intolerable for many real-world applications. Edge computing fulfills the sub-millisecond requirement of 5G applications and reduces energy consumption by around 30-40%, which attributes up to 5x lesser energy consumption as compared to accessing the cloud.
Edge computing and 5G improve network performance to support and deploy different real-time AI applications, such as AI based real-time video analytics depend on low latency data transmission.
Edge Computing vs. Fog Computing
Fog Computing is a term introduced by Cisco, it is closely related to Edge computing. The concept of Fog computing is based on extending the cloud to be closer to the IoT end-devices with the aim to improve latency and security by performing computations near the network edge.
The main difference between fog and edge computing pertains to where the data is processed: in edge computing, data is processed either directly on the devices to which the sensors are attached or on gateway devices physically very close to the sensors; in the fog model, data is processed further away from the edge, on devices connected using a local area network (LAN).
Deep Learning at the Edge
Performing deep learning tasks typically requires a lot of computational power and a massive amount of data. Low-power IoT devices, such as typical cameras, are continuous sources of data. However, their limited storage and compute capabilities make them unsuitable for training and inference of deep learning models.
Edge AI solves this by combining Deep Learning and Edge Computing. Therefore edge servers are placed near those end devices and used for deploying deep learning models that operate on IoT-generated data.
Edge AI Applications
With Edge AI, it becomes possible to power real-time edge AI applications.
- Smart Vision including computer vision applications such as live video analytics to power AI vision systems in multiple industries. Intel developed special co-processors named Visual Processing Units to power high-performance computer vision applications to the edge devices.
- Smart Energy applications such as connected wind farms. A study examined the data management and processing costs of a remote wind farm using a cloud-only system versus a combined edge-cloud system. The wind farm uses several data-producing sensors and devices such as video surveillance cameras, security sensors, access sensors for employees, and sensors on wind turbines. The edge-cloud system turned out to be 36% less expensive as opposed to the cloud-only system, while the volume of data required to be transferred was reduced by 96%.
- Healthcare applications such as remote surgery and diagnostics, as well as monitoring of patient vital signs and data. Doctors can use a remote platform to operate surgical tools from a distance where they feel safe and comfortable.
- Entertainment applications include virtual reality, augmented reality, and mixed reality, such as streaming video contents to virtual reality glasses. The size of such glasses can be reduced by offloading computation from the glasses to edge servers. Microsoft recently introduced HoloLens, a holographic computer that is built onto a headset for an augmented reality experience. Microsoft aims to design standard computing, data analysis, medical imaging, and gaming-at-the-edge tools using the HoloLens.
- Smart Factories applications, such as smart machines, aim to improve safety and productivity. Operators can use a remote platform to operate heavy machines, particularly those located at hard-to-reach and unsafe places, from a safe and comfortable place.
- Intelligent transportation systems, whereby drivers can share or gather information from traffic information centers to avoid vehicles that are in danger or stop abruptly, in a real-time manner in order to avoid accidents. In addition, unmanned vehicles can sense their surroundings and move safely in an autonomous manner.
Edge computing is necessary for real-world AI applications because the traditional cloud computing model is not suitable for AI applications that are computationally intensive and require massive amounts of data.
We recommend you read the following articles that cover related topics:
- Read about Edge Intelligence in 2021
- Learn about Privacy-preserving Deep Learning for Computer Vision
- An easy-to-understand guide to Deep Face Recognition
- What you need to know about Self-Supervised Learning
- Examples and Methods of Deep Reinforcement Learning