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Computing cycles continue to migrate away from on-premise systems. Instead, computing is being moved to either centralized cloud systems or decentralized systems on the edge.
Over the last decade the trend has been to move applications to the cloud. The cloud reduces the demands and resources needed to support on-premise systems, typically is competitively priced, scalable, and often provides greater availability and collaboration.
The rise of Edge Computing has been more recent. Edge computing addresses the need to respond quickly to data created in the field. Edge computers are able to process large amounts of data in real time and avoid the latency and access issues involved in communicating with a centralized server. As systems based on AI algorithms are increasingly used, the deployment of these systems, which typically require the processing of large quantities of real-time data, are particularly well-suited for the edge.
Bridget Karlin, vice president at IBM Services Global, said that “we will see an increase in Edge computing due to the sheer quantity of instances compared to centralized cloud centers. IBM estimates that there are some 15 billion intelligent devices in the market today, and IDC forecasts that by 2025 that will grow to 150 billion — resulting in unprecedented volumes of data.”
Lewis Carr, senior director at Actian, said that the “edge will overtake cloud in terms of sheer horsepower, data collected, and even number of cycles on data processing and analytics operations applied to that data locally at the edge — provided we take the edge to mean end-to-end across the various tiers of the edge.”
Lian Jye Su, Principal Analyst at ABI Research, said that “as enterprises start to look for AI solutions in the areas of image and object recognition, autonomous material handling, predictive maintenance, and human-machine interface for end devices, they need to resolve concerns around data privacy, power efficiency, low latency, and strong on-device computing performance. Edge AI will be the answer to this. By integrating an AI chipset designed to perform high-speed inference and quantized federated learning or collaborative learning models, edge AI brings task automation and augmentation to device and sensor levels across various sectors. So much that it will grow and surpass the cloud AI chipset market in 2025.”