@inproceedings{55fec1056a164deaaa01692cf94be977,
title = "Quantization Effects of Deep Neural Networks on a FPGA platform",
abstract = "In this paper, a quantization method for a FPGA platform is applied on three different deep neural networks (DNNs) for classification, detection and semantic segmentation tasks. The FPGA platform is based on a Xilinx Zynq UltraScale+ ZU5EV. The quantization process is done with the Vitis AI Quantizer tool of the AMD Vitis AI Environment. After a short description of the used DNNs, datasets and the technique behind the quantization process, the results are presented for different bit-widths of the quantized DNNs. The evaluation includes a post-training quantization of weights and activations in INT8 format with performance measurements in frames per second (FPS) on the target platform.",
keywords = "classification, deep learning, detection, FPGA, neural networks, quantization, segmentation",
author = "Daniel Pohl and Birgit Vogel-Heuser and Marius Kruger and Markus Echtler",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 7th IEEE International Conference on Industrial Cyber-Physical Systems, ICPS 2024 ; Conference date: 12-05-2024 Through 15-05-2024",
year = "2024",
doi = "10.1109/ICPS59941.2024.10640013",
language = "English",
series = "2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems, ICPS 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems, ICPS 2024",
}