论文标题
AM-DCGAN：深度卷积生成对抗网络的模拟回忆硬件加速器
AM-DCGAN: Analog Memristive Hardware Accelerator for Deep Convolutional Generative Adversarial Networks
论文作者
论文摘要
生成对抗网络（GAN）是一种众所周知的计算复杂算法，需要在软件实施中具有标志性的计算资源，包括大量培训数据。这使得它在带有常规微处理器硬件的边缘设备中实现了缓慢而艰巨的任务。在本文中，我们建议使用模拟域中的回忆神经网络加速计算密集型GAN。我们提出了基于使用180nm CMOS技术模拟的CMOS-MEMRISTIVE卷积和反向倾斜网络的深卷积GAN（DCGAN）的完全模拟硬件设计。
Generative Adversarial Network (GAN) is a well known computationally complex algorithm requiring signficiant computational resources in software implementations including large amount of data to be trained. This makes its implementation in edge devices with conventional microprocessor hardware a slow and difficult task. In this paper, we propose to accelerate the computationally intensive GAN using memristive neural networks in analog domain. We present a fully analog hardware design of Deep Convolutional GAN (DCGAN) based on CMOS-memristive convolutional and deconvolutional networks simulated using 180nm CMOS technology.
