The rise of deep learning has led to various successful attempts to apply deep neural networks (DNNs) for important networking tasks such as intrusion detection. Yet, running DNNs in the network control plane, as typically done in existing proposals, suffers from high latency that impedes the practicality of such approaches. This paper introduces NetNN, a novel DNN-based intrusion detection system that runs completely in the network data plane to achieve low latency. NetNN adopts raw packet information as input, avoiding complicated feature engineering. NetNN mimics the DNN dataflow execution by mapping DNN parts to a network of programmable switches, executing partial DNN computations on individual switches, and generating packets carrying intermediate execution results between these switches. We implement NetNN in P4 and demonstrate the feasibility of such an approach. Experimental results show that NetNN can improve the intrusion detection accuracy to 99% while meeting the real-time requirement.

The emergence of programmable data planes (PDPs) has paved the way for in-network computing (INC), a paradigm wherein networking devices actively participate in distributed computations. However, PDPs are still a niche technology, mostly available to network operators, and rely on packet-processing DSLs like P4. This necessitates great networking expertise from INC programmers to articulate computational tasks in networking terms and reason about their code. To lift this barrier to INC we propose a unified compute interface for the data plane. We introduce $\mathrm{C} / \mathrm{C}++$ extensions that allow INC to be expressed as kernel functions processing in-flight messages, and APIs for establishing INC-aware communication. We develop a compiler that translates kernels into P4, and thin runtimes that handle the required network plumbing, shielding INC programmers from low-level networking details. We evaluate our system using common INC applications from the literature.

In-network computing is gaining momentum as programmable switches are increasingly employed for compute acceleration. Designed for packet processing, data plane programming languages force developers to express compute in networking terms, resulting in a complex, error-prone practice. We envision the unification of switch and host programming and propose the Net Compute Language (NCL), a C/C++ extension for expressing computational kernels for switches to execute. NCL implements Compute Centric Communication (C3), our proposed programming model for INC under which, point-to-point primitives are augmented to carry out computations. We motivate our approach with real-world use cases and discuss the technical challenges for its realization.