  Network softwarization has revolutionized the architecture of cellular
wireless networks. State-of-the-art container based virtual radio access
networks (vRAN) provide enormous flexibility and reduced life cycle management
costs, but they also come with prohibitive energy consumption. We argue that
for future AI-native wireless networks to be flexible and energy efficient,
there is a need for a new abstraction in network softwarization that caters for
neural network type of workloads and allows a large degree of service
composability. In this paper we present the NeuroRAN architecture, which
leverages stateful function as a user facing execution model, and is
complemented with virtualized resources and decentralized resource management.
We show that neural network based implementations of common transceiver
functional blocks fit the proposed architecture, and we discuss key research
challenges related to compilation and code generation, resource management,
reliability and security.
