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| Home > Publications database > Quantization Effects of Artificial Neural Networks for Embedded Edge-Computing Applications |
| Preprint | FZJ-2026-04161 |
; ; ; ; ; ; ; ;
2026
arXiv
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Please use a persistent id in citations: doi:10.48550/ARXIV.2511.05479
Abstract: 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 Train- ing (QAT), and ultra-low-bit Binary Neural Networks (BNNs) with respect to la- tency and resource usage. Our results demonstrate that PTQ and QAT are easily implementable solutions achieving a four-fold reduction in memory usage for U- shaped CNN (U-Net) architectures, whereas BNNs are better suitable for use cases with challenging latency requirements. For the training of non-differentiable custom BNNs , we propose a novel, hardware-constrained learning approach using Genetic Algorithms (GAs). We showcase a Look-Up Table (LUT)-based BNN architecture suitable for direct conversion to Very High-Speed Integrated Circuit Hardware De- scription Language (VHDL) via the HCL4BNN framework. This method achieves nanosecond-scale inference latencies at 10 ns to 15 ns without requiring specialized Digital Signal Processor (DSP) or Block RAM (BRAM) resources.
Keyword(s): Neural and Evolutionary Computing (cs.NE) ; Instrumentation and Detectors (physics.ins-det) ; FOS: Computer and information sciences ; FOS: Physical sciences
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