Problem
Research questions and friction points this paper is trying to address.
Energy-Efficiency
FFT-based CNNs
Adaptive Floating-Point Quantization
CPU-FPGA platform
Energy-Efficient Visual Inspection with FFT-Based CNNs and Adaptive Floating-Point Quantization
🤖 AI Summary
本文研究了基于FFT的CNN在工业CPU-FPGA平台上的低精度浮点算术，通过自适应FP8量化和两种优化方法提高能效与准确性。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
FFT-based CNNs
Adaptive FP8 Quantization
Layer-wise Bias Optimization
Energy Efficiency
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