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.
Spafl: Communi cation- efficient federated learning with sparse models and low comp utational overhead
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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.