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@INPROCEEDINGS{Aksoy:1055111,
      author       = {Aksoy, Alperen and Fleitmann, Sarah and Bekman, Ilja and
                      Dorosti, Qader and Vogelbruch, Jan-Friedrich and Dimitrov,
                      Vesselin and Hader, Fabian and van Waasen, Stefan},
      title        = {{E}mbedded {A}rtificial {N}eural {N}etworks for
                      {E}nergy-{R}estricted {E}dge-{C}omputing {A}pplications},
      reportid     = {FZJ-2026-01868},
      year         = {2026},
      abstract     = {The development of energy-efficient and fast machine
                      learning methods plays an increasingly important role in
                      experimental physics, where data analysis and control tasks
                      often need to operate under strict resource constraints. In
                      these contexts, machine learning models can automate complex
                      calibration and analysis tasks while enabling on-device data
                      processing close to the experimental sensors.One
                      representative application presented on this poster concerns
                      the automated calibration of semiconductor spin qubits,
                      while the outlook highlights extensions toward
                      edge-computing approaches in detector systems.The automated
                      calibration of quantum dots is a key prerequisite for
                      realizing scalable quantum computers. In particular, the
                      analysis of charge stability diagrams, used to detect charge
                      transitions in quantum dots, represents a complex and
                      time-consuming task. Neural networks, especially U-Net
                      architectures, offer the potential to automate this process
                      by reliably recognizing relevant patterns in simulated and
                      experimental measurement data. State-of-the-art networks
                      have already been successfully trained for this
                      purpose.However, there remains significant potential for
                      optimization to enable space- and energy-efficient
                      integration close to the quantum bits within the cryostat.We
                      have investigated the use of quantized neural networks for
                      energy-efficient quantum dot calibration. The goal is to
                      analyze the impact of post-training quantization and
                      quantization-aware training on detection quality, as well as
                      the general effects of quantization on memory requirements
                      and inference speed. Three U-Nets with different
                      architectures, parameter counts, and input dimensions serve
                      as model bases, applied to simulated charge stability
                      diagrams. The results show that appropriate quantization
                      strategies can reduce memory usage without significantly
                      affecting detection quality.The findings of this work
                      contribute to the integration of energy-efficient machine
                      learning methods into experimental quantum computing
                      environments, thereby supporting overall
                      scalability.Building on these results, the approach is
                      extended toward the use of binarized neural networks (BNNs)
                      to push energy efficiency and faster inference even further.
                      Within edge computing applications, current efforts focus on
                      implementing and demonstrating such networks on FPGA
                      hardware, aiming to exploit binary-weight computation and
                      hardware-level parallelism for minimal latency and power
                      consumption. Beyond quantum dot calibration, these methods
                      are also being investigated for other scientific
                      applications, such as the autonomous self-triggering radio
                      detection of extensive air showers, highlighting the broader
                      potential of hardware-embedded AI for resource-constrained
                      experimental environments.},
      month         = {Mar},
      date          = {2026-03-03},
      organization  = {deRSE26 - 6th conference for Research
                       Software Engineering $\&$ 1st Stuttgart
                       Research Software Day, Stuttgart
                       (Germany), 3 Mar 2026 - 5 Mar 2026},
      subtyp        = {After Call},
      keywords     = {Quantum Dot Calibration (Other) / Energy-Efficient Machine
                      Learning (Other) / Quantized Neural Networks (Other) / U-Net
                      (Other) / Edge Computing (Other) / FPGA (Other) / Binary
                      Neural Networks (Other) / Model Compression (Other) /
                      Real-Time Inference (Other)},
      cin          = {PGI-4},
      cid          = {I:(DE-Juel1)PGI-4-20110106},
      pnm          = {5234 - Emerging NC Architectures (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5234},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.5281/ZENODO.18770020},
      url          = {https://juser.fz-juelich.de/record/1055111},
}