标题： FENIX：通过FPGA增强的可编程交换机实现网络内DNN推理 显示英文标题

标题： FENIX: Enabling In-Network DNN Inference with FPGA-Enhanced Programmable Switches

摘要： 机器学习（ML）在网络安全数据平面中用于高级流量分析的应用日益增多。 然而，现有的解决方案（如FlowLens、N3IC和BoS）仍然难以同时实现低延迟、高吞吐量和高准确性。 为了解决这些挑战，我们提出了FENIX，一种混合的网络内ML系统，它在可编程交换机ASIC上执行特征提取，并在FPGA上执行深度神经网络推理。 FENIX引入了一个数据引擎，利用概率令牌桶算法来控制特征流的发送速率，有效解决了可编程交换机ASIC和FPGA之间的吞吐量差距。 此外，FENIX设计了一个模型引擎，以在网络中实现高精度的深度神经网络推理，克服了在资源受限的交换机芯片上部署复杂模型的困难。 我们在一个集成Tofino ASIC和ZU19EG FPGA的可编程交换机平台上实现了FENIX，并在真实世界的网络流量数据集上进行了评估。 我们的结果表明，FENIX实现了微秒级的推理延迟和多太比特的吞吐量，且硬件开销较低，在主流网络流量分类任务中准确率超过95%，性能优于最先进方法。 显示英文摘要

摘要： Machine learning (ML) is increasingly used in network data planes for advanced traffic analysis. However, existing solutions (such as FlowLens, N3IC, and BoS) still struggle to simultaneously achieve low latency, high throughput, and high accuracy. To address these challenges, we present FENIX, a hybrid in-network ML system that performs feature extraction on programmable switch ASICs and deep neural network inference on FPGAs. FENIX introduces a Data Engine that leverages a probabilistic token bucket algorithm to control the sending rate of feature streams, effectively addressing the throughput gap between programmable switch ASICs and FPGAs. In addition, FENIX designs a Model Engine to enable high-accuracy deep neural network inference in the network, overcoming the difficulty of deploying complex models on resource-constrained switch chips. We implement FENIX on a programmable switch platform that integrates a Tofino ASIC and a ZU19EG FPGA directly and evaluate it on real-world network traffic datasets. Our results show that FENIX achieves microsecond-level inference latency and multi-terabit throughput with low hardware overhead, and delivers over 95\% accuracy on mainstream network traffic classification tasks, outperforming SOTA.