RIWWER
Zusammenfassung
Das Projekt RIWWER erforscht, wie man das Duisburger Abwassernetz durch Digitalisierung und mit Hilfe von KI resilienter gegenüber Starkregenereignissen machen kann. Damit trägt es wesentlich zum Gewässer- und Umweltschutz bei.
Unser Fachgebiet ist im Projekt mit Tianheng Ling vertreten. Konkret kümmert sie sich um die Optimierung und Komprimierung von Algorithmen und Modellen für die verwendeten eingebetteten Systeme.
Das vom BMWK geförderte Projekt ist eine Kooperation mit einer Reihe von Partnern aus anderen Hochschulen und dem öffentlichen und privaten Sektor.
Die Ergebnisse des Projekts sollen außerdem über die in Arbeit befindliche VDI-Expertenempfehlung 4900 zur Digitalisierung der Abwasserwirtschaft in ganz Deutschland beitragen.
Laufzeit
| Projektstart | 01.10.2022 |
| Projektende | 30.09.2025 |
Das Ziel
Das Ziel des Projektes ist die Verringerung der Umweltbelastung durch ungeklärte Abwässer bei Regenfällen.
Weiterführende Links
Pressemitteilung des Fraunhofer-Instituts IMS zum Projektauftakt
Projektpartner
- Stadt Duisburg
- Wirtschaftsbetriebe Duisburg
- VDI e.V.
- Fraunhofer-Institut für Mikroelektronische Schaltungen und Systeme
- Universität Duisburg-Essen
- Technologie- und Innovationsmanagement (TIM) der RWTH Aachen
- Berliner Hochschule für Technik
- KROHNE Messtechnik GmbH (Duisburg)
- HST Systemtechnik GmbH & Co. KG (Meschede)
- Okeanos Smart Data Solutions GmbH (Bochum)
Publikationen
- Ling, Tianheng; Qian, Chao; Schiele, Gregor
Resource-Aware Mixed-Precision Quantization for Enhancing Deployability of Transformers for Time-Series Forecasting on Embedded FPGAs
In: Mobile and Ubiquitous Systems: Computing, Networking and Services; Proceedings / 21st EAI International Conference (MobiQuitous 2024); Oslo, Norway; November 12–14, 2024 / Soylu, Ahmet; Liu, Fan; Mitra, Karan; Zhang, Yan; Grønli, Tor-Morten (Hrsg.). Cham: Springer Nature Switzerland, 2026, S. 361 – 381
DOI - Ling, Tianheng; Qian, Chao; Klann, Theodor Mario; Hoever, Julian; Einhaus, Lukas; Schiele, Gregor
Configurable Multi-Layer Perceptron-Based Soft Sensors on Embedded Field Programmable Gate Arrays: Targeting Diverse Deployment Goals in Fluid Flow Estimation
In: Sensors, Jg. 25, 2025, Nr. 1, 83
DOI - Qian, Chao; Ling, Tianheng; Cichiwskyj, Christopher; Schiele, Gregor
Configuration-aware approaches for enhancing energy efficiency in FPGA-based deep learning accelerators
In: Journal of Systems Architecture, Jg. 163, 2025, 103410
DOI - Ling, Tianheng; Qian, Chao; Hasler, Lukas Johannes; Schiele, Gregor
Automating Versatile Time-Series Analysis with Tiny Transformers on Embedded FPGAs
In: IEEE Computer Society Annual Symposium on VLSI: ISVLSI 2025 / ISVLSI 2025: 28th IEEE Computer Society Annual Symposium on VLSI, 6-9 July 2025, Greece / Institute of Electrical and Electronics Engineers (IEEE) (Hrsg.). Piscataway: Institute of Electrical and Electronics Engineers (IEEE), 2025
DOI Online Volltext - Singh, Vipin; Ling, Tianheng; Chiaburu, Teodor; Biessmann, Felix
Evaluating Time Series Models for Urban Wastewater Management: Predictive Performance, Model Complexity and Resilience
In: 10th International Conference on Smart and Sustainable Technologies, SpliTech 2025 / 10th International Conference on Smart and Sustainable Technologies, SpliTech 2025, 16-20 June 2025,Bol and Split, Croatia. Piscataway: Institute of Electrical and Electronics Engineers Inc., 2025
DOI Online Volltext - Ling, Tianheng; Singh, Vipin; Qian, Chao; Biessmann, Felix; Schiele, Gregor
Automated Energy-Aware Time-Series Model Deployment on Embedded FPGAs for Resilient Combined Sewer Overflow Management
In: Proceedings of the IEEE International Smart Cities Conference (ISC2 2025) / International Smart Cities Conference (ISC2 2025); 06-09 October 2025; Patras, Greece. Piscataway: Institute of Electrical and Electronics Engineers (IEEE), 2025
DOI Online Volltext - Ling, Tianheng; Qian, Chao; Schiele, Gregor
On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data
In: Mobile and Ubiquitous Systems: Computing, Networking and Services; Proceedings, Part II / 20th EAI International Conference, MobiQuitous 2023, November 14–17, 2023, Melbourne, Australia / Zaslavsky, Arkady; Ning, Zhaolong; Kalogeraki, Vana; Georgakopoulos, Dimitrios; Chrysanthis, Panos K. (Hrsg.). Cham: Springer Nature, 2024, S. 529 – 537
DOI - Qian, Chao; Cichiwskyj, Christopher; Ling, Tianheng; Schiele, Gregor
Idle is the New Sleep: Configuration-Aware Alternative to Powering Off FPGA-Based DL Accelerators During Inactivity
In: Architecture of Computing Systems: Proceedings / 37th International Conference, ARCS 2024, Potsdam, Germany, May 14–16, 2024 / Fey, Dietmar; Stabernack, Benno; Lankes, Stefan (Hrsg.). Berlin, Germany: Springer Science and Business Media Deutschland GmbH, 2024, S. 161 – 176
DOI - Qian, Chao; Ling, Tianheng; Schiele, Gregor
Exploring energy efficiency of LSTM accelerators: A parameterized architecture design for embedded FPGAs
In: Journal of Systems Architecture, Jg. 152, 2024, 103181
DOI - Ling, Tianheng; Hoever, Julian; Qian, Chao; Schiele, Gregor
FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization
In: 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) / IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 11-15 March 2024, Biarritz, France. Piscataway: IEEE, 2024
DOI Online Volltext - Ling, Tianheng; Qian, Chao; Schiele, Gregor
Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoT
In: 2024 IEEE Annual Congress on Artificial Intelligence of Things (AIoT): Proceedings / IEEE Annual Congress on Artificial Intelligence of Things (IEEE AIoT), 24-26 July 2024, Melbourne / IEEE (Hrsg.). New York: IEEE, 2024, S. 38 – 44
DOI Online Volltext - Ling, Tianheng; Qian, Chao; Schiele, Gregor
Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation
In: 2024 IEEE Annual Congress on Artificial Intelligence of Things (AIoT): Proceedings / IEEE Annual Congress on Artificial Intelligence of Things (IEEE AIoT), 24-26 July 2024, Melbourne / IEEE (Hrsg.). New York: IEEE, 2024, S. 248 – 249
DOI Online Volltext - Qian, Chao; Ling, Tianheng; Schiele, Gregor
Enhancing Energy-Efficiency by Solving the Throughput Bottleneck of LSTM Cells for Embedded FPGAs
In: Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part I / International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022 / Koprinska, Irena; Mignone, Paolo; Guidotti, Riccardo (Hrsg.). Cham: Springer, 2023, S. 594 – 605
DOI - Ling, Tianheng; Schiele, Gregor
On-Device AI: Quantization-Aware Training of Transformers in Time-Series
In: Proceedings of the 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops 2023) / International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops); 13-17 March 2023; Atlanta, USA. Piscataway: Institute of Electrical and Electronics Engineers (IEEE), 2023, S. 235 – 236
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