A token-routing multi-modal transformer reduces inference latency by 86.2%, GPU memory by 35%, and FLOPs by 80% for beamforming tasks with negligible accuracy loss while enabling proactive handover on a real testbed dataset.
MVX-ViT: Multimodal col laborative perception for 6G V2X network management decisions using vi sion transformer
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Transformer Architecture with Minimal Inference Latency for Multi-Modal Wireless Networks
A token-routing multi-modal transformer reduces inference latency by 86.2%, GPU memory by 35%, and FLOPs by 80% for beamforming tasks with negligible accuracy loss while enabling proactive handover on a real testbed dataset.