As the world’s data requirements exponentially increase, expanding the cloud capacity cannot solve the problem since servers require large amounts of energy. Enter Edge AI.
By 2025, it is estimated that humans will produce a total of 175 zettabytes of data. That number will touch a staggering 2142 zettabytes by 2035.
Modern data needs larger computing prowess to process these large volumes. Most data is processed using cloud computing technology today. While the cloud is an impressive technology and can easily be accessed, it is still not free of problems and comes with its own challenges. For example, cloud security is a constant risk for any business. Data breaches and cloud outages have cost companies millions lately, proving to be greatly damaging. In November, Google had a cloud outage where users were denied access to all its services, Meta’s servers went down for more than three hours in October causing a global shutdown. As the data requirements exponentially increase, these cloud servers will only find themselves under greater pressure, much more than ever before.
Simply expanding cloud capacity cannot solve the problem since servers require large amounts of energy. Tech companies are now turning to edge computing and edge AI.
What is Edge AI?
Edge AI is a technological architecture, in which AI models are processed locally on devices at the edge of the network. The created machine learning algorithms are processed locally ‘on edge’, i.e on the device itself, or on a nearby server. Edge AI setups only require a single microprocessor paired with sensors, not compulsorily an internet connection, and can process data, making predictions in real-time. Although the technology already existed, it is now becoming a part of several different industries. For example, intelligent devices such as smartphones use edge tech for a variety of tasks. According to Markets and Markets, the global edge AI software market is anticipated to grow from $590 million to $1.83 billion by 2026.
Amazon says that the cost of inference i.e. when a model is running in full force over the cloud to make predictions constitutes up to 90 per cent of machine learning infrastructure costs. Edge AI, on the other hand, requires little to no cloud infrastructure beyond the initial development phase. A model might be trained in the cloud but deployed on an edge device, where it runs without server infrastructure.
An Edge AI hardware generally falls into three categories: on-premise AI servers, intelligent gateways, and edge devices. Edge AI servers are systems that come along with specialised components designed to support a wide range of model inferencing and training applications. Gateways usually sit between edge devices, servers, and other elements of the implemented network, and edge devices perform AI inference and training functions performed in real-time on the device itself.
Edge AI And Its Implementation
The intention of deploying AI hardware at the edge is often centered around certain requirements in data transmission, storage, and privacy considerations. Edge AI is not intended to replace cloud computing but rather to complement and improve it. One of the several ways it does so is by improving latency across connected devices. In an industrial or manufacturing enterprise with thousands of sensors, it might not be highly practical to send vast amounts of sensor data to the cloud, get the analytics carried out, and then return the results to the manufacturing location. Sending such data across would require huge amounts of bandwidth, as well as cloud storage, and might potentially expose sensitive information.
In such cases, edge AI can be incorporated, using connected devices and AI applications in environments where internet connections may not be reliable as in the case of deep-sea drilling rigs or research vessels. Its low latency makes it well-suited to time-sensitive tasks such as predictive failure detection and smart shelf systems for retail using computer vision.
Edge AI that has been incorporated into a microchip can provide little to no latency, usually in sub-milliseconds as the data never leaves the device. This decentralised nature of the technology allows machine-learning algorithms to run autonomously. There are lesser risks of internet outages or poor mobile phone reception. Since data does need to leave the device, edge AI chips greatly reduce the amount of information that is transmitted, improving efficiency in turn.
On a production line, integrated edge AI chips can analyse data at unprecedented speeds. Analysing sensor data and detecting deviations from the norm in real-time and quick succession allows workers to replace the machinery before it is expected to fail. Real-time analytics also triggers the automatic decision-making process, notifying workers of what can happen further. Video analytics when embedded with such technology can allow instant notification of problems on the production line.
The advantages of implementing edge AI have been noticed already by business and industry leaders. Pitchbook reports that the investment in the edge computing semiconductor industry has grown by 74 per cent% over the last 12 months, bringing the total investment to $5.8 billion.
Challenges in Edge AI
While edge AI offers several advantages compared to cloud-based AI technologies, it isn’t without its own challenges. Data when stored locally can sometimes lead to more locations to protect, with increased physical access allowing for different kinds of cyberattacks. Some experts argue the decentralised nature of edge computing leads to increased security. Computing power is limited at the edge, which in turn restricts the number of AI tasks that can be performed at one time. Large and complex models usually have to be simplified before they are deployed to edge AI hardware, in some cases reducing their expected accuracy. Emerging hardware promises to alleviate some of the compute limitations at the edge, with several startups developing chips that are specially customised for AI workloads. Tech giants like Microsoft, Amazon, Intel, and Asus also offer hardware platforms for edge AI deployment, an example being Amazon’s DeepLens wireless video camera for deep learning. Gartner predicts that more than half of large enterprises will have at least six edge computing use cases deployed by the end of 2023.
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