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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2511.05479 |
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| _version_ | 1866910244004691968 |
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| author | Aksoy, Alperen Bekman, Ilja Dimitrov, Vesselin Dorosti, Qader Eguzo, Chimezie Fleitmann, Sarah Grewing, Christian Hader, Fabian Zambanini, Andre van Waasen, Stefan |
| author_facet | Aksoy, Alperen Bekman, Ilja Dimitrov, Vesselin Dorosti, Qader Eguzo, Chimezie Fleitmann, Sarah Grewing, Christian Hader, Fabian Zambanini, Andre van Waasen, Stefan |
| contents | This paper examines the use of Quantized Neural Networks (QNNs) for two resource-constrained scientific applications: automated calibration of semi-conductor quantum bits (qubits) and scientific particle detectors. We evaluate the trade-offs between Post-Training Quantization (PTQ), Quantization-Aware Training (QAT), and ultra-low-bit Binary Neural Networks (BNNs) with respect to latency and resource usage. Our results demonstrate that PTQ achieves a four-fold reduction in memory usage for U-shaped CNN (U-Net) architectures while maintaining or slightly enhancing segmentation accuracy (e.g. from 89% to 90% for a small U-Net with 447 parameters). For the training of non-differentiable custom BNNs , we propose a novel, hardware-constrained learning approach using Genetic Algorithms (GAs). We showcase a LUT-based BNN architecture suitable for direct conversion to VHDL via the HCL4BNN framework. This method achieves nanosecond-scale inference latencies (10-15 ns) without requiring specialized DSP or BRAM resources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05479 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantization Effects of Artificial Neural Networks for Embedded Edge-Computing Applications Aksoy, Alperen Bekman, Ilja Dimitrov, Vesselin Dorosti, Qader Eguzo, Chimezie Fleitmann, Sarah Grewing, Christian Hader, Fabian Zambanini, Andre van Waasen, Stefan Neural and Evolutionary Computing Instrumentation and Detectors This paper examines the use of Quantized Neural Networks (QNNs) for two resource-constrained scientific applications: automated calibration of semi-conductor quantum bits (qubits) and scientific particle detectors. We evaluate the trade-offs between Post-Training Quantization (PTQ), Quantization-Aware Training (QAT), and ultra-low-bit Binary Neural Networks (BNNs) with respect to latency and resource usage. Our results demonstrate that PTQ achieves a four-fold reduction in memory usage for U-shaped CNN (U-Net) architectures while maintaining or slightly enhancing segmentation accuracy (e.g. from 89% to 90% for a small U-Net with 447 parameters). For the training of non-differentiable custom BNNs , we propose a novel, hardware-constrained learning approach using Genetic Algorithms (GAs). We showcase a LUT-based BNN architecture suitable for direct conversion to VHDL via the HCL4BNN framework. This method achieves nanosecond-scale inference latencies (10-15 ns) without requiring specialized DSP or BRAM resources. |
| title | Quantization Effects of Artificial Neural Networks for Embedded Edge-Computing Applications |
| topic | Neural and Evolutionary Computing Instrumentation and Detectors |
| url | https://arxiv.org/abs/2511.05479 |