Split-AI proposes a paradigm shift in which Neural
Networks (NN)-relevant tasks are distributed across multiple
networking entities. The capabilities envisaged for 6G mobile
networking such as In-Network Computing (INC) can facilitate
a smooth deployment of this technique. 6G’s Access Nodes
(ANs) and User Plane Functions (UPFs) will be able to execute
parts of an NN while performing their traditional functions
(e.g. transmitting packets). Recent studies show that INC-assisted
SplitAI has the potential to enhance Key Performance Indicators
(KPIs) like inference time, network traffic load, and UE energy
consumption. The incorporation of Split-AI in 6G networks
needs to fulfill significant design and performance requirements
such as optimal partitioning strategies for distributing NN parts
across network elements. Additionally, the 6G User Plane must
enable the dynamic and flexible allocation and deployment of
these split NN parts. In this work we: i) propose possible
enhancements for the Control Plane (CP) and UP, to make
possible the integration of INC-assisted Split-AI in 6G networks;
ii) present the design of a Proof-Of-Concept (PoC) simulation
model aimed at demonstrating the feasibility of the technique
with a comprehensive study on the impact that different NN
partitioning options have on the network utilization, inference
time, and the resources of both UE and network entities involved.