Published: 18 January 2024
Advancements in edge hardware and software are supporting the movement of more complex, scalable AI workloads to a broader asset base. Product leaders must differentiate in this maturing market by investing in emerging technologies that support new use cases and scalable business value.
Advancements in edge hardware (such as neuromorphic computing and AI chips) and software (such as vision accelerators and container management) are supporting the movement of more complex, scalable AI workloads to the edge.
The combinatorial use of AI and technologies such as IoT platforms, blockchain, vision accelerators and orbital AI is delivering intelligence to additional endpoints and supporting more use-case innovation/expansion.
Edge techniques such as model compression, synthetic data, and privacy-enhancing techniques are focused on solving key adoption challenges — model size and hardware constraints, lack of training data, and privacy, helping to drive edge adoption.
Edge market maturation is
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Analysts:
Danielle Casey, Eric Goodness, Akhil Singh, Bill Ray, Wataru Katsurashima, Vibha Chitkara, Bart Willemsen, John Santoro, Scot Kim, Alan Priestley, Sylvain Fabre, Nick Ingelbrecht, Ray Valdes, Menglin Cao, Tuong Nguyen, Thomas Bittman
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