CNN卷积神经网络在FPGA加速器上的实现：深度学习算法从软件到硬件部署的实战教程,CNN卷积神经网络在FPGA加速器上的实现：软件到硬件部署的学习项目通过仿真验证，适用于FPGA和CNN深度学习算

EcdbbRinWUBcZIP卷积神经网  1.41MB

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ZIP 卷积神经网 大约有12个文件
  1. 1.jpg 135.9KB
  2. 2.jpg 302.97KB
  3. 加速卷积神经网络的实践之旅在当今的时代深度学习模型.docx 44.48KB
  4. 卷积神经网络加速器实现从软件到硬件的.docx 45.29KB
  5. 卷积神经网络加速器实现小型.html 614.92KB
  6. 卷积神经网络加速器实现小型加速器实.html 613.77KB
  7. 卷积神经网络加速器实现小型化探讨随着深度学习.docx 43.97KB
  8. 卷积神经网络加速器实现小型摘要本文介绍了一种.docx 20.13KB
  9. 卷积神经网络加速器实现小型版图.docx 43.32KB
  10. 卷积神经网络加速器实现小型版的技术分析随着.docx 44.48KB
  11. 本文主要介绍了卷积神经网络在加速器上的.docx 42.52KB
  12. 标题从软件到硬件小型卷积神经网络加速器的实现与仿真.docx 15.9KB

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CNN卷积神经网络在FPGA加速器上的实现：深度学习算法从软件到硬件部署的实战教程,CNN卷积神经网络在FPGA加速器上的实现：软件到硬件部署的学习项目 通过仿真验证，适用于FPGA和CNN深度学习算法，高效配置，可量化参数存储于片上RAM，采用Vivado开发环境。,CNN卷积神经网络 FPGA加速器实现(小型)CNN FPGA加速器实现(小型) 仿真通过，用于foga和cnn学习 通过本工程可以学习深度学习cnn算法从软件到硬件fpga的部署。 网络软件部分基于tf2实现，通过python导出权值，硬件部分verilog实现，纯手写代码，可读性高，高度参数化配置，可以针对速度或面积要求设置不同加速效果。 参数量化后存储在片上ram，基于vivado开发。 直接联系提供本项目实现中所用的所有软件( python)和硬件代码( verilog)。 ,核心关键词： CNN卷积神经网络; FPGA加速器; 小型CNN FPGA实现; 仿真通过; 深度学习; cnn算法; 软件到硬件fpga部署; 网络软件; tf2; python导出权值; 硬件部分verilog实现; 参数量化; 片
<link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90434198/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90434198/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">CNN<span class="_ _0"> </span><span class="ff2">卷积神经网络<span class="_ _0"> </span></span>FPGA<span class="_ _0"> </span><span class="ff2">加速器实现：从软件到硬件的深度学习之旅</span></div><div class="t m0 x1 h2 y2 ff2 fs0 fc0 sc0 ls0 ws0">一、引言</div><div class="t m0 x1 h2 y3 ff2 fs0 fc0 sc0 ls0 ws0">随着<span class="_ _1"></span>深度<span class="_ _1"></span>学习<span class="_ _1"></span>技术<span class="_ _1"></span>的飞<span class="_ _1"></span>速发展<span class="_ _1"></span>，卷<span class="_ _1"></span>积神<span class="_ _1"></span>经网<span class="_ _1"></span>络（<span class="_ _1"></span><span class="ff1">CNN</span>）<span class="_ _1"></span>已经<span class="_ _1"></span>成为<span class="_ _1"></span>许多<span class="_ _1"></span>领域<span class="_ _1"></span>中的<span class="_ _1"></span>关键<span class="_ _1"></span>技术<span class="_ _1"></span>。</div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">然而，<span class="_ _2"></span>传统的<span class="_ _0"> </span><span class="ff1">CNN<span class="_ _0"> </span></span>算法在处理大规模数据时，<span class="_ _2"></span>往往需要强大的计算能力和大量的存储空间。</div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">为了解<span class="_ _1"></span>决这<span class="_ _1"></span>个问<span class="_ _1"></span>题，我<span class="_ _1"></span>们可<span class="_ _1"></span>以利用<span class="_ _3"> </span><span class="ff1">FPGA</span>（现<span class="_ _1"></span>场可<span class="_ _1"></span>编程<span class="_ _1"></span>门阵列<span class="_ _1"></span>）来<span class="_ _1"></span>加速<span class="_ _3"> </span><span class="ff1">CNN<span class="_ _0"> </span></span>算法的<span class="_ _1"></span>运行<span class="_ _1"></span>。</div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">本文将介绍一个从小型<span class="_ _0"> </span><span class="ff1">CNN<span class="_ _0"> </span></span>到<span class="_ _0"> </span><span class="ff1">FPGA<span class="_"> </span></span>加速器的实现过程，<span class="_ _4"></span>通过仿真通过，<span class="_ _4"></span>用于<span class="_ _3"> </span><span class="ff1">foga<span class="_ _0"> </span></span>和<span class="_ _0"> </span><span class="ff1">cnn</span></div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">学习，使读者能够了解深度学习<span class="_ _0"> </span><span class="ff1">CNN<span class="_ _0"> </span></span>算法从软件到硬件<span class="_ _0"> </span><span class="ff1">FPGA<span class="_"> </span></span>的部署。</div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">二、软件部分：基于<span class="_ _0"> </span><span class="ff1">TF2<span class="_ _0"> </span></span>的<span class="_ _0"> </span><span class="ff1">CNN<span class="_"> </span></span>实现</div><div class="t m0 x1 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">软件部分主要基于<span class="_ _0"> </span><span class="ff1">TensorFlow 2</span>（<span class="ff1">TF2</span>）实现<span class="_ _0"> </span><span class="ff1">CNN<span class="_"> </span></span>算法。通过<span class="_ _0"> </span><span class="ff1">Python<span class="_ _0"> </span></span>编程语言，我们可以</div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">轻松地构建、训练和导出<span class="_ _0"> </span><span class="ff1">CNN<span class="_"> </span></span>模型。在<span class="_ _0"> </span><span class="ff1">TF2<span class="_ _0"> </span></span>中，我们可以使用高级<span class="_ _0"> </span><span class="ff1">API<span class="_ _0"> </span></span>来定义网络结构，</div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">并利用其强大的计算图来训练模型。<span class="_ _5"></span>此外，<span class="_ _5"></span>我们还可以导出模型的权值，<span class="_ _5"></span>以便在硬件部分中</div><div class="t m0 x1 h2 yc ff2 fs0 fc0 sc0 ls0 ws0">使用。</div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">三、硬件部分：<span class="ff1">FPGA<span class="_ _0"> </span></span>加速器实现</div><div class="t m0 x1 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">硬件部分主要使用<span class="_ _0"> </span><span class="ff1">Verilog<span class="_"> </span></span>语言实现<span class="_ _0"> </span><span class="ff1">CNN<span class="_ _0"> </span></span>的<span class="_ _0"> </span><span class="ff1">FPGA<span class="_ _0"> </span></span>加速器。<span class="_ _6"></span>这是一个纯手写的代码，<span class="_ _6"></span>具有高</div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">可读性，<span class="_ _5"></span>并且高度参数化配置，<span class="_ _5"></span>可以针对速度或面积要求设置不同的加速效果。<span class="_ _5"></span>在硬件设计</div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">中，我<span class="_ _1"></span>们采<span class="_ _1"></span>用了<span class="_ _1"></span>参数<span class="_ _1"></span>量化技<span class="_ _1"></span>术，<span class="_ _1"></span>将<span class="_ _0"> </span><span class="ff1">CNN<span class="_"> </span></span>模型<span class="_ _1"></span>的权值<span class="_ _1"></span>存储<span class="_ _1"></span>在片<span class="_ _1"></span>上<span class="_ _0"> </span><span class="ff1">RAM<span class="_"> </span></span>中，以<span class="_ _1"></span>便快<span class="_ _1"></span>速访<span class="_ _1"></span>问和</div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">计算。此外，我们还使用了基于<span class="_ _0"> </span><span class="ff1">Vivado<span class="_"> </span></span>的开发工具来进行设计和仿真。</div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">四、实现过程</div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">1. <span class="_ _0"> </span><span class="ff2">参数设计与量化<span class="_ _5"></span>：<span class="_ _5"></span>首先，我们需要根据<span class="_ _0"> </span><span class="ff1">CNN<span class="_"> </span></span>模型的需求，设计并量化参数。这包括确定</span></div><div class="t m0 x1 h2 y14 ff1 fs0 fc0 sc0 ls0 ws0">CNN<span class="_"> </span><span class="ff2">的层数、每层的滤<span class="_ _1"></span>波器数量<span class="_ _1"></span>、滤波器<span class="_ _1"></span>大小等参数<span class="_ _1"></span>，以及将<span class="_ _1"></span>权值进行<span class="_ _1"></span>量化以便存<span class="_ _1"></span>储在片</span></div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">上<span class="_ _0"> </span><span class="ff1">RAM<span class="_"> </span></span>中。</div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">2. Verilog<span class="_ _0"> </span><span class="ff2">代码编写<span class="_ _4"></span>：<span class="_ _4"></span>根据设计好的参数和量化结果，我们使用<span class="_ _0"> </span><span class="ff1">Verilog<span class="_ _0"> </span></span>语言编写<span class="_ _0"> </span><span class="ff1">FPGA<span class="_ _0"> </span></span>加速</span></div><div class="t m0 x1 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0">器的硬件代码。这个代码实现了<span class="_ _0"> </span><span class="ff1">CNN<span class="_ _0"> </span></span>算法中的卷积、池化等操作，并且高度优化以提高运</div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">行速度。</div><div class="t m0 x1 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">3. <span class="_ _0"> </span><span class="ff2">仿真与测试<span class="_ _4"></span>：<span class="_ _4"></span>使用<span class="_ _0"> </span><span class="ff1">Vivado<span class="_ _0"> </span></span>等开发工具对硬件代码进行仿真和测试。确保其功能正确且性</span></div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">能达到预期。</div><div class="t m0 x1 h2 y1b ff1 fs0 fc0 sc0 ls0 ws0">4. <span class="_ _0"> </span><span class="ff2">片上<span class="_ _0"> </span></span>RAM<span class="_ _0"> </span><span class="ff2">配置：将量化的权值导入片上<span class="_ _0"> </span></span>RAM<span class="_ _0"> </span><span class="ff2">中，以便在运行时快速访问。</span></div><div class="t m0 x1 h2 y1c ff1 fs0 fc0 sc0 ls0 ws0">5. <span class="_ _0"> </span><span class="ff2">部署与运行：将<span class="_ _0"> </span></span>FPGA<span class="_ _0"> </span><span class="ff2">加速器部署到目标设备上，并运行<span class="_ _0"> </span></span>CNN<span class="_ _0"> </span><span class="ff2">算法进行实际的应用。</span></div><div class="t m0 x1 h2 y1d ff2 fs0 fc0 sc0 ls0 ws0">五、项目特点</div><div class="t m0 x1 h2 y1e ff1 fs0 fc0 sc0 ls0 ws0">1. <span class="_ _0"> </span><span class="ff2">高可读性<span class="_ _5"></span>：<span class="_ _5"></span>本项目的硬件代码是纯手写的<span class="_ _0"> </span><span class="ff1">Verilog<span class="_"> </span></span>代码，具有高可读性，方便后续的维护</span></div><div class="t m0 x1 h2 y1f ff2 fs0 fc0 sc0 ls0 ws0">和修改。</div><div class="t m0 x1 h2 y20 ff1 fs0 fc0 sc0 ls0 ws0">2. <span class="_ _0"> </span><span class="ff2">高度参数化<span class="_ _1"></span>配置：本<span class="_ _1"></span>项目的<span class="_ _1"></span>硬件设<span class="_ _1"></span>计是高<span class="_ _1"></span>度参数化<span class="_ _1"></span>配置的<span class="_ _1"></span>，可以<span class="_ _1"></span>针对速<span class="_ _1"></span>度或面积<span class="_ _1"></span>要求设</span></div><div class="t m0 x1 h2 y21 ff2 fs0 fc0 sc0 ls0 ws0">置不同的加速效果。</div><div class="t m0 x1 h2 y22 ff1 fs0 fc0 sc0 ls0 ws0">3. <span class="_ _0"> </span><span class="ff2">存储优化<span class="_ _2"></span>：<span class="_ _2"></span>通过参数量化技术，<span class="_ _5"></span>将权值存储在片上<span class="_ _0"> </span><span class="ff1">RAM<span class="_"> </span></span>中，<span class="_ _5"></span>提高了访问速度和计算效率。</span></div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>
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