论文标题
基于卷积神经网络的FPGA深度学习加速度概述
Overview of FPGA deep learning acceleration based on convolutional neural network
论文作者
论文摘要
近年来，深度学习变得越来越成熟，并且作为深度学习中常用的算法，卷积神经网络已被广泛用于各种视觉任务。过去，基于深度学习算法的研究主要依赖于GPU和CPU等硬件。但是，随着FPGA的不断增长的开发，这两个现场可编程逻辑门阵列都已成为结合各种神经网络深度学习算法的主要实现硬件平台本文是一篇评论文章，主要介绍相关的理论和相关理论和算法。它总结了基于卷积神经网络的几种现有FPGA技术的应用方案，并主要介绍加速器的应用。同时，它总结了某些加速器对逻辑资源的利用不足或对内存带宽的利用不足，因此他们无法获得最佳性能。
In recent years, deep learning has become more and more mature, and as a commonly used algorithm in deep learning, convolutional neural networks have been widely used in various visual tasks. In the past, research based on deep learning algorithms mainly relied on hardware such as GPUs and CPUs. However, with the increasing development of FPGAs, both field programmable logic gate arrays, it has become the main implementation hardware platform that combines various neural network deep learning algorithms This article is a review article, which mainly introduces the related theories and algorithms of convolution. It summarizes the application scenarios of several existing FPGA technologies based on convolutional neural networks, and mainly introduces the application of accelerators. At the same time, it summarizes some accelerators' under-utilization of logic resources or under-utilization of memory bandwidth, so that they can't get the best performance.
