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Edge Computing Market
The acquisition of Xnor.ai by Apple will give a competitive advantage at the device edge.
Apple's recent acquisition of XNOR.AI will help it compete with Amazon Web Services, Microsoft, and Google for growing prospects in artificial intelligence (AI) and edge computing. There is a growing interest among all of these companies in the so-called "device edge," and the potential to leverage advanced data processing capabilities and sophisticated AI algorithms within portable devices such as smartphones, watches, and headphones, or other technology within a home environment, and factory or store equipment outfitted with internet-connected sensors.
Notable strengths and competitive differentiators associated with Xnor.ai's processor technology include lower energy consumption when compared to Apple's existing AI device processers, as well as the use of solar power, which helps to alleviate the drain on device battery power that is invariably associated with service consumption.
Apple's recent $200 million acquisition of Seattle-based Xnor.ai places it among a growing number of firms pursuing the prospects connected with artificial intelligence (AI) and edge computing.
Apple's acquisition of Xnor.ai will help it compete with the leading cloud service providers, Amazon Web Services (AWS), Microsoft, and Google, all of which are focused on leveraging the benefits of AI and edge computing Market and compete with Apple in a variety of product and service segments, including streaming media, virtual reality, and cloud computing.
The utilization of computer processing, data storage, and analytics capabilities near to where data is gathered and digital content and applications are consumed is referred to as edge computing. Edge computing's purpose is to allow and support digital services and applications that demand high performance and low latency (i.e. the amount of time it takes for a packet of digital data to travel from one point to another). These include self-driving cars, high-definition (HD) video, and immersive multimedia. Edge computing technology may be used in a variety of settings, such as secondary and tertiary data centers, factories and retail shops, and telecom network operator base stations.
Combining edge computing with AI technologies such as machine learning (ML) opens the door to a slew of new applications, including those that use image analytics (for monitoring, surveillance, and face and expression recognition), audio analytics (for speech recognition and event detection), and environmental sensor analytics (e.g. measuring fitness and health levels, and measuring building vibration, noise, and temperature levels).
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