FPGA Acceleration of Generative Adversarial Networks for Image Reconstruction

Dimitrios Danopoulos, Konstantinos Anagnostopoulos, C. Kachris, D. Soudris
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Abstract

Accurate and efficient Machine Learning algorithms are of vital importance to many problems, especially on classification or clustering tasks. In recent years, a new class of Machine Learning has been introduced called Generative Adversarial Network (GAN) which relies on two neural networks: a generative network (generator) and a discriminative network (discriminator). These two networks compete with each other with aim to generate new data such as images. For example, a GAN is capable of reconstructing an image which is filled by noise or has some regions damaged. Image reconstruction has found its application in the field of computer vision, augmented reality, human computer interaction and animation as well as medical imaging. However, this type of algorithm requires many MAC (multiply-accumulate) operations and high power consumption to operate. In this work, we implement an Image reconstruction algorithm with GANs, specifically as a case study we train a model capable of restoring clothing images based on the fashion-MNIST dataset. Additionally, we implement and accelerate it on a Xilinx FPGA SoC which as platforms are proven to address these kind of problems very efficiently in terms of performance and power. The design also achieves better performance and power efficiency from CPU and GPU with 0.013 ms average reconstruction time per image and 43 db PSNR on the FPGA quantized configuration.
生成对抗网络图像重建的FPGA加速
准确、高效的机器学习算法对于解决许多问题至关重要,尤其是在分类或聚类任务上。近年来,一种新的机器学习类型被引入,称为生成对抗网络(GAN),它依赖于两个神经网络:生成网络(生成器)和判别网络(鉴别器)。这两个网络相互竞争,目的是产生新的数据,如图像。例如,GAN能够重建被噪声填充或某些区域受损的图像。图像重建在计算机视觉、增强现实、人机交互和动画以及医学成像等领域都有应用。然而,这种算法需要进行大量的MAC(乘累加)运算,且功耗高。在这项工作中,我们使用gan实现了一种图像重建算法,特别是作为一个案例研究,我们训练了一个能够基于fashion-MNIST数据集恢复服装图像的模型。此外,我们在赛灵思FPGA SoC上实现并加速了它,该平台已被证明可以在性能和功耗方面非常有效地解决这些问题。该设计还实现了CPU和GPU更好的性能和功耗效率,平均每张图像重构时间为0.013 ms, FPGA量化配置的PSNR为43 db。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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