Switches and other network devices process data at wire rate, meaning they can handle packets at the maximum capacity of the data-link connection. Modern switches separate functionality into two layers: the control plane for slower, high-level decisions (e.g., forwarding tables) and the data plane, which is the hardware-accelerated path through which each packet is actually processed (e.g., port forwarding). In recent years, with the rise of Programmable Data Planes (PDPs), a major research trend has explored how deep neural network (DNN) models can be leveraged to address long-standing network challenges (e.g., flow classification, anomaly detection) by deploying deep learning models within PDPs.