Tactile Internet based operations, e.g., telesurgery, rely on end-to-end closed loop control for accuracy and corrections. The feedback and control are subject to network latency and loss. We design two edge intelligence algorithms hosted at P4 programmable end switches. These algorithms locally compute and command corrective signals, thereby dispense the feedback signals from traversing the network to the other ends and save on control loop latency and network load. We implement these algorithms entirely on data plane on Netronome Agilio SmartNICs using P4. Our first algorithm, $\textit{pose correction}$, is placed at the edge switch connected to an industrial robot gripping a tool. The round trip between transmitting force sensor array readings to the edge switch and receiving correct tip coordinates at the robot is shown to be less than $100~\mu s$. The second algorithm, $\textit{tremor suppression}$, is placed at the edge switch connected to the human operator. It suppresses physiological tremors of amplitudes smaller than $100~\mu m$ which not only improves the application's performance but also reduces the network load up to $99.9\%$. Our solution allows edge intelligence modules to seamlessly switch between the algorithms based on the tasks being executed at the end hosts.

翻译：基于触觉互联网的操作（例如远程手术）依赖端到端闭环控制实现精度与纠偏，其反馈与控制过程受网络延迟和数据丢失影响。我们设计了两种托管于P4可编程终端交换机的边缘智能算法：这些算法在本地计算并执行修正信号，从而避免反馈信号穿越网络传输至对端，显著降低了控制环路延迟和网络负载。我们利用P4在Netronome Agilio SmartNIC的数据平面上完整实现了上述算法。第一种算法为$\textit{姿态修正}$，部署于连接工业机械臂（夹持工具）的边缘交换机。实验表明，从力传感器阵列读数传至边缘交换机到机械臂接收正确末端坐标的往返时间小于$100~\mu s$。第二种算法为$\textit{震颤抑制}$，部署于连接操作人员的边缘交换机。该算法可抑制振幅小于$100~\mu m$的生理性震颤，不仅提升了应用性能，还使网络负载降低高达$99.9\%$。我们的解决方案支持边缘智能模块根据终端主机执行的任务在算法间无缝切换。