arXiv:2610.27749v1 Announce Type: new Abstract: Deep neural network (DNN) partitioning enables distributed inference by dividing model computation across interconnected devices. Existing approaches have largely focused on optimizing DNN deployment over infrastructure with fixed computing and communication capacities. Yet when these resources cannot adequately support inference demands, optimizing deployment alone is insufficient: the supporting infrastructure must also be provisioned. Crucially, deployment and provisioning are inherently coupled. How a DNN is partitioned determines where capacity is needed, while capacity provisioning can fundamentally change the preferred deployment. Motivated by this interdependence, we investigate the co-design of DNN deployment and infrastructure capacity for pipelined inference. Despite the coupling between these decisions, we reveal an efficiently solvable structure: on acyclic networks, the problem admits an exact shortest-path reformulation, enabling globally optimal joint design in polynomial time. Numerical evaluations demonstrate the benefits of co-design over treating deployment and provisioning separately, highlighting the potential of inference-driven infrastructure planning.