GANPU: A 135TFLOPS/W Multi-DNN Training Processor for GANs with Speculative Dual-Sparsity Exploitation

📋 论文概要

该论文提出了一种名为GANPU的多DNN训练处理器，用于生成对抗网络（GAN）的训练，通过推测性双稀疏性开发实现了135TFLOPS/W的高能效。它解决了GAN训练中计算密集和内存访问频繁的问题，显著提升了移动设备上GAN应用的性能。

💡 主要创新点

• 提出推测性双稀疏性开发技术，同时利用权重和激活值的稀疏性来减少计算和内存访问。
• 设计了一种多DNN训练架构，支持生成器和判别器的交替训练，优化了流水线效率。
• 实现了135TFLOPS/W的峰值能效，在GAN训练任务中达到领先水平。

🏷 关键词

📄 原文摘要

image style transfer to synthetic voice generation [1]. GAN applications on mobile devices, such as face-to-Emoji conversion and super-resolution imaging, enable more engaging user interaction. As shown in Fig. 7.4.1, a GAN consists of 2 competing deep neural networks (DNN): a generator and a discriminator. The discriminator is trained, while the generator is fixed, to distinguish whether the generated image is real or fake. On the other hand, the generator is trained to generate fake images to fool the discriminator. The minimax rivalry between the 2 sub-DNNs enables the model to generate high-quality images, difficult even for humans to distinguish. GANs have complex architectures, incorporating multiple DNNs to be trained in a single model. This is also the case for Siamese networks, actor-critic or multiagent reinforcement learning models. The di

👥 作者与机构

Sanghoon Kang, Donghyeon Han, Juhyoung Lee, Dongseok Im,

Sangyeob Kim, Soyeon Kim, Hoi-Jun Yoo KAIST, Daejeon, Korea Generative adversarial networks (GAN) have a wide range of applications, from