Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices
This work addresses efficient ML deployment for resource-constrained edge IoT platforms, but it is incremental as it applies existing methods to a specific domain.
The study tackled deploying Split Learning on FPGA-based edge IoT devices for river water quality monitoring using LSTM models, showing that design choices must align with application needs like speed, power, or resource constraints.
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.