mlsanityData to 14 Oct 2026

Quantization Effects of Artificial Neural Networks for Embedded Edge-Computing Applications

arXiv 2511.05479 ↗First posted 7 Nov 2025cs.NE physics.ins-detcited by 0

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Abstract (arXiv)

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.

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References · 10

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,593
  3. 2016XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networkscited by 849
  4. 2016Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activationscited by 426
  5. 2020Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectorscited by 80
  6. 2021hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devicescited by 62
  7. 2020LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applicationscited by 57
  8. 2019LUTNet: Rethinking Inference in FPGA Soft Logiccited by 28
  9. 2024Differentiable Weightless Neural Networkscited by 28
  10. 2024Design Space Exploration for Particle Detector Read-out Implementations in Matlab and Simulink on the Example of the SHiP SBTcited by 1

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  3. 2022Low- and Mixed-Precision Inference Accelerators
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  5. 2025JAQ: Joint Efficient Architecture Design and Low-Bit Quantization with Hardware-Software Co-Exploration
  6. 2022PIM-QAT: Neural Network Quantization for Processing-In-Memory (PIM) Systems
  7. 2022Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks
  8. 2023On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks