In-Line Any-Depth Deep Neural Networks Using P4 Switches

Abstract

In-network function offloading using programmable data plane languages like P4 offers computational resource savings and efficient operations at the network edge. However, the deployment of ML functions in P4~switches exploiting no additional hardware remains a challenge. In this paper, we tackle the challenges in deploying Deep Neural Networks (DNNs) within programmable network devices, introducing a novel distillation based on Look-Up Tables (LUTs). The proposed method maps quantized DNNs into a cascaded arrangement of LUTs without loss in accuracy and independently of the quantized network depth. The developed approach is demonstrated in two network function use cases: a cyber security use case focused on mitigating Distributed Denial of Service attacks and a malicious activity classification task in IoT Networks. Experimental results show a trade-off between model’s accuracy, expressed in terms of F1-Score and the computational demands, influenced by bit size and number of LUTs. In addition, the latency for deploying these models ranged from 54ns to 112ns, showing the method's practical applicability in network functions.

Keywords

• Cyber security
• deep neural networks
• look-up tables
• P4
• traffic classification