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
资源内容介绍
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>