Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation

TL;DR

This work addresses real-time wastewater flow estimation on resource-constrained edge devices using DL-based soft sensors. It proposes an end-to-end workflow spanning wastewater-specific data collection, automated FPGA accelerator deployment via ElasticAI.Creator, and hardware design optimizations for energy efficiency. Prior research shows that FPGA-based inference can offer substantial speedups but requires careful trade-offs between precision and resources; quantization and hardware pipelines emerge as key levers. The contributions enable scalable, automated deployment of on-device DL models for wastewater flow monitoring, reducing the need for FPGA expertise and promoting practical, energy-efficient field deployment.

Abstract

Executing flow estimation using Deep Learning (DL)-based soft sensors on resource-limited IoT devices has demonstrated promise in terms of reliability and energy efficiency. However, its application in the field of wastewater flow estimation remains underexplored due to: (1) a lack of available datasets, (2) inconvenient toolchains for on-device AI model development and deployment, and (3) hardware platforms designed for general DL purposes rather than being optimized for energy-efficient soft sensor applications. This study addresses these gaps by proposing an automated, end-to-end solution for wastewater flow estimation using a prototype IoT device.

Figures (3)

• Figure 1: Workflow of Flow Estimation with Soft Sensors from Application to Edge Devices
• Figure 2: Illustration of GUNT HM162 Experimental Flume
• Figure 3: Elastic Node V5 SE with Soft Sensor