Adaptive Swin Transformer Partitioning over AI-RAN Networks

TL;DR

This paper explores transformer-based split inference for real-time video detection over 5G networks, introducing adaptive partitioning, an activation compression pipeline, and end-to-end system validation on a real testbed.

Contribution

It extends throughput-aware adaptive splitting to Swin Transformers, introduces an activation compression method, and demonstrates a complete system for efficient, real-time inference over AI-RAN networks without retraining.

Findings

Achieved practical split execution for transformer-based vision models.

Reduced uplink payload significantly with activation compression.

Validated end-to-end system performance on a real-time detection workload.

Abstract

This paper demonstrates the feasibility of transformer-based split inference for real-time video object detection over dynamic 5G AI-RAN networks. We extend throughput-aware adaptive splitting from CNNs to a Swin Transformer backbone and show that practical split execution is achievable for transformer-based vision models without retraining. To address the large intermediate activations inherent to transformers, we introduce an efficient, accuracy-preserving activation compression pipeline that substantially reduces uplink payload. The complete system -- including adaptive split selection, transformer inference, and compression -- is implemented and validated end-to-end on a real-time detection workload, with distributed UPF (dUPF) integration further reducing user-plane latency and improving runtime stability. Extensive measurements on an NVIDIA Aerial-based AI-RAN testbed jointly account for inference and 5G communication energy, quantifying the latency-energy-privacy trade-offs in realistic deployments.