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Handling the Baggage of Edge AI

Edge AI’s growth hinges on reconciling two diametrically opposed domains: Machine Learning and Embedded
Over stuffed baggage

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By Junko Yoshida

What’s at stake:
Everyone loves talking about Edge AI, but without mentioning the persistent gap between the AI and embedded worlds. Edge AI designers are caught in a never-ending cycle of ‘optimization’, pressed to fit neural network models and achieve acceptable accuracy on their hardware. They are desperate for tools to lighten their load. At stake is the scaling of edge AI deployment.

Edge AI today stands at “this uncomfortable junction,” said Evan Petridis, CEO at Eta Compute, in a recent interview with the Ojo-Yoshida Report. Edge AI straddles two domains – machine learning (ML) and embedded. These two distinctly different fields share neither the same language nor design philosophies.

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