标题： 基于定点算术和泰勒展开的P4可编程FPGA SmartNIC上的实时网络内机器学习 显示英文标题

标题： Real-Time In-Network Machine Learning on P4-Programmable FPGA SmartNICs with Fixed-Point Arithmetic and Taylor

摘要： 随着机器学习（ML）应用成为现代网络操作的关键部分，对于网络可编程性的需求不断增加，这使得低延迟的ML推理成为可能，用于服务质量（QoS）预测和网络安全中的异常检测等任务。 ML模型通过动态权重调整提供适应性，使协议无关数据包处理器（P4）可编程的FPGA SmartNIC成为研究网络内机器学习（INML）的理想平台。 这些设备提供高吞吐量、低延迟的数据包处理能力，并可以通过控制平面进行动态重新配置，从而在网络边缘直接灵活集成ML模型。 本文探讨了将P4编程范式应用于神经网络和回归模型，其中权重和偏置存储在控制平面表查找中。 这种方法实现了在网络边缘灵活的可编程性和可重新训练的ML模型的高效部署，而无需依赖交换机级别的核心基础设施。 显示英文摘要

摘要： As machine learning (ML) applications become integral to modern network operations, there is an increasing demand for network programmability that enables low-latency ML inference for tasks such as Quality of Service (QoS) prediction and anomaly detection in cybersecurity. ML models provide adaptability through dynamic weight adjustments, making Programming Protocol-independent Packet Processors (P4)-programmable FPGA SmartNICs an ideal platform for investigating In-Network Machine Learning (INML). These devices offer high-throughput, low-latency packet processing and can be dynamically reconfigured via the control plane, allowing for flexible integration of ML models directly at the network edge. This paper explores the application of the P4 programming paradigm to neural networks and regression models, where weights and biases are stored in control plane table lookups. This approach enables flexible programmability and efficient deployment of retrainable ML models at the network edge, independent of core infrastructure at the switch level.