在FPGA边缘设备上优化LSTM模型的分割学习，兼顾性能与资源消耗。
Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices
- 将LSTM模型分拆部署于边缘与云端，降低本地计算压力。
- 实测表明不同配置下可实现速度、功耗与资源的灵活权衡。
- 适用于对算力敏感的河流水质监测等物联网场景。
分割学习（Split Learning, SL）作为一种高效的分布式机器学习范式，适用于物联网-云系统。然而，在资源受限的边缘物联网平台部署SL仍面临挑战，需在模型性能与处理、内存、能耗之间取得平衡。本文针对基于现场可编程门阵列（FPGA）的实际边缘物联网平台，开展了一项实用研究，将SL框架应用于基于循环神经网络（RNN）的时间序列处理模型。以河流水质监测为背景，使用真实数据，在给定的FPGA边缘平台中训练、优化并部署长短期记忆（LSTM）模型，采用多种SL配置。结果表明，设计选择必须与具体应用需求对齐——无论是最大化速度、最小化功耗，还是优化资源约束。该研究为实际部署提供了重要指导。
原文摘要 · Abstract (English)
Split Learning (SL) recently emerged as an efficient paradigm for distributed Machine Learning (ML) suitable for the Internet Of Things (IoT)-Cloud systems. However, deploying SL on resource-constrained edge IoT platforms poses a significant challenge in terms of balancing the model performance against the processing, memory, and energy resources. In this work, we present a practical study of deploying SL framework on a real-world Field-Programmable Gate Array (FPGA)-based edge IoT platform. We address the SL framework applied to a time-series processing model based on Recurrent Neural Networks (RNNs). Set in the context of river water quality monitoring and using real-world data, we train, optimize, and deploy a Long Short-Term Memory (LSTM) model on a given edge IoT FPGA platform in different SL configurations. Our results demonstrate the importance of aligning design choices with specific application requirements, whether it is maximizing speed, minimizing power, or optimizing for resource constraints.
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