Everyone, it seems, is now talking about how they’re planning to integrate AI into their devices, their factories, their workflows and, well, everything. But how they actually plan to make that happen isn’t always clear. Part of the challenge, of course, is that different workloads and different environments require different types of solutions.
For those looking to integrate AI-powered capabilities into edge computing-based offerings, there are a relatively broad range of ways to ac...
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Artificial intelligence (AI) is making its way onto a multitude of applications at a rapid pace. Seventy-three percent of U.S. companies use AI in some aspect of their business and almost a quarter (22 percent) of technology companies are aggressively pursuing integration of AI across a wide variety of devices.
As AI continues to permeate modern technology, tailoring on-device AI to meet specific use cases and device requirements becomes imperative as the need for real-time processing, analysis,...
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A new generation of AI-based edge computing applications demand a wide range of performance requirements. But how can developers build edge solutions that consume little power and occupy minimal footprint at low cost without compromising performance?
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