# Automated Energy-Aware Time-Series Model Deployment on Embedded FPGAs for Resilient Combined Sewer Overflow Management

arXiv: https://arxiv.org/abs/2508.13905 · HTML: https://mlsanity.com/paper/2508.13905/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [A White Paper on Neural Network Quantization](https://mlsanity.com/paper/2106.08295/) (2021)
- [Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoT](https://mlsanity.com/paper/2407.11041/) (2024)
- [ElasticAI: Creating and Deploying Energy-Efficient Deep Learning Accelerator for Pervasive Computing](https://mlsanity.com/paper/2409.09044/) (2024)
- [Automating Versatile Time-Series Analysis with Tiny Transformers on Embedded FPGAs](https://mlsanity.com/paper/2505.17662/) (2025)
- [Data-driven Modeling of Combined Sewer Systems for Urban Sustainability: An Empirical Evaluation](https://mlsanity.com/paper/2408.11619/) (2024)
- [Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation](https://mlsanity.com/paper/2407.05102/) (2024)

## Built on by (most cited first, 2 of 2)

- [StrikeWatch: Wrist-worn Gait Recognition with Compact Time-series Models on Low-power FPGAs](https://mlsanity.com/paper/2510.24738/) (2025)
- [A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge](https://mlsanity.com/paper/2605.10592/) (2026)

## Abstract (arXiv)

Extreme weather events, intensified by climate change, increasingly challenge aging combined sewer systems, raising the risk of untreated wastewater overflow. Accurate forecasting of sewer overflow basin filling levels can provide actionable insights for early intervention, helping mitigating uncontrolled discharge. In recent years, AI-based forecasting methods have offered scalable alternatives to traditional physics-based models, but their reliance on cloud computing limits their reliability during communication outages. To address this, we propose an end-to-end forecasting framework that enables energy-efficient inference directly on edge devices. Our solution integrates lightweight Transformer and Long Short-Term Memory (LSTM) models, compressed via integer-only quantization for efficient on-device execution. Moreover, an automated hardware-aware deployment pipeline is used to search for optimal model configurations by jointly minimizing prediction error and energy consumption on an AMD Spartan-7 XC7S15 FPGA. Evaluated on real-world sewer data, the selected 8-bit Transformer model, trained on 24 hours of historical measurements, achieves high accuracy (MSE 0.0376) at an energy cost of 0.370 mJ per inference. In contrast, the optimal 8-bit LSTM model requires significantly less energy (0.009 mJ, over 40x lower) but yields 14.89% worse accuracy (MSE 0.0432) and much longer training time. This trade-off highlights the need to align model selection with deployment priorities, favoring LSTM for ultra-low energy consumption or Transformer for higher predictive accuracy. In general, our work enables local, energy-efficient forecasting, contributing to more resilient combined sewer systems. All code can be found in the GitHub Repository (https://github.com/tianheng-ling/EdgeOverflowForecast).