AI 中文总结
本文提出释放触发通信计算耦合（RTCC）方法，在物理GPU上实现多智能体协同感知DNN的上行完成触发推理，可降低检测延迟且保持检测性能。
AI 中文摘要
本文研究集中式多智能体协同感知中无线输入完成与图形处理器（GPU）执行的耦合关系。除了对完成触发重叠的概念性处理外，本文在物理GPU上实现并验证了面向协同感知深度神经网络（DNN）的完整执行路径：每个编码器分支在对应输入传输完成后立即释放，同时保留原始融合依赖关系与推理映射。由此产生的释放触发通信计算耦合（RTCC）通过主机到设备（H2D）暂存、CUDA同步及保留依赖的分支调度，传播已验证的无线完成事件，且与因果无线调度器兼容。结合轨迹驱动无线到达、物理GPU执行及实测有向无环图（DAG）评估的实验显示，RTCC在不同通信负载与调度器下降低了完整检测延迟，且相较于传统执行保留了相同的检测输出与平均精度性能。
英文摘要
This paper investigates the coupling between wireless input completion and graphics processing unit (GPU) execution in centralized multi-agent cooperative perception. Specifically, beyond the conceptual treatment of completion-triggered overlap, a complete execution path is realized and validated on a physical GPU for a cooperative-perception deep neural network (DNN). Each encoder branch is released immediately upon completion of its corresponding input transmission, while the original fusion dependencies and inference mapping are preserved. The resulting release-triggered communication computation coupling (RTCC) propagates validated wireless completion events through host-to-device (H2D) staging, CUDA synchronization, and dependency-preserving branch dispatch, while remaining compatible with causal wireless schedulers. Experiments combining trace-driven wireless arrivals, physical GPU execution, and measured-DAG evaluation show that RTCC reduces complete-detection latency across different communication loads and schedulers, while preserving identical detection outputs and average-precision performance relative to conventional execution.
