论文提出 IoD：将 AI 推理拆分为 5G 用户面网络 API

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Abstract:Supporting edge Artificial Intelligence (AI) inference as a network service is challenging because current mobile systems can transport tensors but do not expose or understand the inference context that gives those tensors operational meaning. Prior studies have addressed this gap through dynamic partitioning, collaborative inference, and split pipelines; however, these solutions remain application-bound, network-blind, or context-free. This work proposes Inference on Demand (IoD), an inference-on-demand abstraction that exposes split inference through a CAMARA-like Application Program Interface (API) and carries a split-aware tensor context in-band across the 5G user plane path. We evaluate the IoD on a programmable user plane function (UPF)-based testbed using a partitionable visual AI workload, showing that adaptive split selection can operate as a network-facing service while avoiding the rigid cost profile of static configurations. Including our findings, split inference becomes more than offloading when the service intent, runtime partitioning, and tensor transport are made visible to the mobile data plane. This opens a path toward network-native AI services, in which programmable telco infrastructure coordinates computation, context, and connectivity for user-facing applications.

Comments:	Accepted for publication in the Proceedings of the IEEE Future Networks World Forum 2026 (Portugal)
Subjects:	Artificial Intelligence (cs.AI)
Cite as:	arXiv:2610.15062 [cs.AI]
	(or arXiv:2610.15062v1 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2610.15062 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Rodrigo Moreira [view email]
[v1] Sat, 10 Oct 2026 16:55:26 UTC (475 KB)