The Essence of Edge Artificial Intelligence
Edge Artificial Intelligence (AI), also known as Edge AI, is making waves in various sectors, particularly in industrial Internet of Things (IoT) applications. This technology, however, extends beyond the realm of industry, finding its place in DevOps, robotics, consumer technology, and more.
Companies like SICK, German Edge Cloud, and tech giants such as Intel, IBM, Adlink Technology, and Synaptics are already leveraging Edge AI in industry. SICK uses industrial AI and sensor technology for real-time defect detection and process control, while German Edge Cloud provides modular Industrial AI solutions for medium-sized enterprises, including intelligent production monitoring and visual quality control.
The appeal of Edge AI lies in its ability to enable devices to make decisions in milliseconds, without the need for an internet connection or the cloud. This local processing capability is a game-changer, especially for real-time data analytics.
Edge AI systems can be run on a wide range of hardware, from central processing units (CPUs) to microcontrollers and advanced neural processing devices. This versatility makes it accessible for various industries, from manufacturing to healthcare and energy.
ClearBlade, a company specializing in edge AI implementation, is making strides in industrial IoT devices, particularly for predictive maintenance. Their inference engine, Onyx, allows for the building of models in multiple languages and with various tools.
Manufacturing facilities are increasingly adopting edge AI technology to optimize their factories and processes. By doing so, they can enhance worker safety and optimize various processes, paving the way for a smart factory.
The market for edge AI hardware is projected to grow significantly, with estimates suggesting it will grow from 920 million units in 2021 to over 2 billion units by 2026. The edge AI chip market alone is expected to grow by $73 billion by 2025.
Startups like Axelera are working towards designing chips that offer high computing performance and usability at a lower price and power consumption. Axelera's chip, for instance, merges the memory with the CPU in what is called 'in-memory computing,' making it highly efficient.
Security is a concern with edge AI, and extra measures such as access control, traffic monitoring, data backup, antivirus and anti-malware software, and encryption are necessary to ensure data integrity and privacy.
Edge AI also offers benefits in healthcare, allowing hospitals and other providers to reap the benefits of AI without transmitting sensitive patient information unnecessarily. This local processing capability is particularly advantageous in smart homes, enabling faster and more secure real-time analytics.
Machine learning algorithms can run directly at the edge of a given network with edge AI, enabling devices to make real-time decisions without depending on the cloud. Edge AI systems typically have three main layers: the device layer, a local edge server, and the cloud.
In essence, Edge AI is the implementation of AI in an edge computing environment, allowing computations to be done on devices where data is created, rather than in a centralized cloud computing facility or offsite data center. This decentralized approach is set to revolutionize industries, making AI more accessible and efficient for all.
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