Enhancing Energy-Efficiency by Solving the Throughput Bottleneck of LSTM Cells for Embedded FPGAs. - Chao Qian 009; Tianheng Ling; Gregor Schiele (PKDD/ECML Workshops (1), 2022)

Energy Efficient LSTM Accelerators for Embedded FPGAs Through Parameterised Architecture Design. - Chao Qian 009; Tianheng Ling; Gregor Schiele (ARCS, 2023)

On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data. - Tianheng Ling; Chao Qian 009; Gregor Schiele (MobiQuitous (2), 2023)

On-Device AI: Quantization-Aware Training of Transformers in Time-Series. - Tianheng Ling; Gregor Schiele (PerCom Workshops, 2023)

ElasticAI: Creating and Deploying Energy-Efficient Deep Learning Accelerator for Pervasive Computing. - Chao Qian 009; Tianheng Ling; Gregor Schiele (PerCom Workshops, 2023)

A Study of Quantisation-aware Training on Time Series Transformer Models for Resource-constrained FPGAs. - Tianheng Ling; Chao Qian 009; Lukas Einhaus; Gregor Schiele (CoRR, 2023)

Idle is the New Sleep: Configuration-Aware Alternative to Powering Off FPGA-Based DL Accelerators During Inactivity. - Chao Qian 009; Christopher Cichiwskyj; Tianheng Ling; Gregor Schiele (ARCS, 2024)

FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization. - Tianheng Ling; Julian Hoever; Chao Qian 009; Gregor Schiele (PerCom Workshops, 2024)

Data-driven Modeling of Combined Sewer Systems for Urban Sustainability: An Empirical Evaluation. - Vipin Singh 003; Tianheng Ling; Teodor Chiaburu; Felix Biessmann (PI-AI@KI, 2024)

Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation. - Tianheng Ling; Chao Qian 009; Gregor Schiele (CoRR, 2024)

Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoT. - Tianheng Ling; Chao Qian 009; Gregor Schiele (CoRR, 2024)

An Automated Approach to Collecting and Labeling Time Series Data for Event Detection Using Elastic Node Hardware. - Tianheng Ling; Islam Mansour; Chao Qian 009; Gregor Schiele (CoRR, 2024)

Resource-aware Mixed-precision Quantization for Enhancing Deployability of Transformers for Time-series Forecasting on Embedded FPGAs. - Tianheng Ling; Chao Qian 009; Gregor Schiele (CoRR, 2024)

Exploring energy efficiency of LSTM accelerators: A parameterized architecture design for embedded FPGAs. - Chao Qian 009; Tianheng Ling; Gregor Schiele (J. Syst. Archit., 2024)

Configuration-aware approaches for enhancing energy efficiency in FPGA-based deep learning accelerators. - Chao Qian 009; Tianheng Ling; Christopher Cichiwskyj; Gregor Schiele (J. Syst. Archit., 2025)

Evaluating Time Series Models for Urban Wastewater Management: Predictive Performance, Model Complexity and Resilience. - Vipin Singh 003; Tianheng Ling; Teodor Chiaburu; Felix Biessmann (CoRR, 2025)

Automating Versatile Time-Series Analysis with Tiny Transformers on Embedded FPGAs. - Tianheng Ling; Chao Qian 009; Lukas Johannes Haßler; Gregor Schiele (CoRR, 2025)