Limited scalability

One of the fundamental requirements of automated systems is the ability to scale with emerging needs and evolve with changing technology. While the current architecture supports the expansion of systems without making significant changes, it is not well designed to support frequent adjustments that characterize artificial intelligence systems.

AI systems are in a constant state of adaptation. As new data and capabilities are consistently integrated, these systems need to constantly adopt new execution models. The von Neumann architecture is not designed to autonomously change as the system learns. Besides, adding new infrastructure to these systems can be challenging.

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The von Neumann computing architecture is not designed with scalability at its core, and it is poorly equipped to support the learning capabilities of artificial intelligence systems. It has become imperative to explore alternative computing architectures.

Neuromorphic computing is one of the architectures that can unlock the true potential of artificial intelligence.