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arXiv · 2608.19837

Energy-Efficient Visual Inspection with FFT-Based CNNs and Adaptive Floating-Point Quantization

Lukas Krupp·Marco Groß·Michael Graichen·Kim Ulrich·Norbert Wehn
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Abstract

This paper investigates reduced-precision floating-point arithmetic for FFT-based CNN inference on an industrial CPU-FPGA platform. We combine FFT-based convolution with adaptive post-training FP8 quantization and evaluate two FPGA-oriented optimization methods: progressive bias adjustment (PBA) within the FFT and layer-wise exponent-bias selection across the CNN. The methods are implemented in a LeNet-5 accelerator using serial radix-$2^2$ SDF FFT modules and evaluated on an industrial fault detection dataset. Results show that weight scaling outperforms PBA, while layer-wise bias optimization increases the accuracy from 80.33% to 84.13% without modifying the datapath width. Compared with CPU-only inference, the FPGA achieves approximately 2.5$\times$ higher energy efficiency.

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Lukas Krupp, Marco Groß, Michael Graichen, Kim Ulrich, Norbert Wehn. 2026-08-20. Energy-Efficient Visual Inspection with FFT-Based CNNs and Adaptive Floating-Point Quantization. https://arxiv.org/abs/2608.19837

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