Exploiting Virtual Layers and Pruning for FPGA-Based Adaptive Traffic Classification

Traffic classification is crucial to many network administration tasks, from resource management to QoS monitoring. While DNNs are the state-of-the-art method for classifying traffic, they are computationally intensive, posing challenges to their adoption within the networking infrastructure. One popular alternative is exploiting the FPGAs in the Smart Network Interface Cards (SmartNICs) to speed up the DNN inference processing. In this context, there are two main obstacles. First, modern networks experience high-volume and very volatile traffic flow, making the design of in-network accelerators difficult. Second, the use of FPGA-enabled SmartNICs involves reconfiguration when changing the classification task, which leads to significant time and energy overheads. In this work, we propose two different but complementary solutions to the aforementioned challenges: the use of pruning, which dynamically removes parts of the DNN to speed up its processing at the cost of controlled accuracy drops, therefore adapting the inference processing to the constantly changing traffic; and Hardware Virtual Layers (HWVL), which eliminate the need for FPGA reconfigurations for seamless and almost instantaneous task switching. Both approaches are combined in the Spyke Framework, improving throughput in up to 1.46× and reducing the energy per inference in up to 1.37× compared to a state-of-the-art FPGA accelerator.