The rapid evolution of internet-connected devices, from personal computers to billions of IoT-enabled smart devices, has created unprecedented opportunities for rich, data-driven applications. However, the growing diversity and heterogeneity of these devices present major challenges in network coordination, data processing, and infrastructure scalability. With increasing demands for real-time, high-fidelity applications across domains like healthcare, mobility, and energy, centralised cloud models face limitations due to latency, bandwidth, and complexity. Emerging trends such as distributed computing, zero-delay networking, and autonomous resource management require new abstractions for dynamic, flexible, and interoperable infrastructures.

HYPER-AI aims to address these challenges across the network continuum by enabling hyper-distributed execution of intensive data-processing applications. It will implement computing swarms as autonomous, self-organised networks of smart nodes, which will be able to dynamically connect, interact, and cooperate, to give targeted applications access to computational, storage, or network services. HYPER-AI will also adopt semantic resource abstraction to unify heterogeneous infrastructures under a common node framework. Finally, its objective is to make smart multi-node deployment scenario design, execution, and monitoring easier, through appropriate AIs for self-configuration, self-healing, self-optimising and self-protecting, at application runtime.