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<title>CMSIS-NN: Convolutional Neural Network Example</title>
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<div class="title">Convolutional Neural Network Example</div> </div>
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<dl class="section user"><dt>Description:</dt><dd></dd></dl>
<dl class="section user"><dt></dt><dd>Demonstrates a convolutional neural network (CNN) example with the use of convolution, ReLU activation, pooling and fully-connected functions.</dd></dl>
<dl class="section user"><dt>Model definition:</dt><dd></dd></dl>
<dl class="section user"><dt></dt><dd>The CNN used in this example is based on CIFAR-10 example from Caffe [1]. The neural network consists of 3 convolution layers interspersed by ReLU activation and max pooling layers, followed by a fully-connected layer at the end. The input to the network is a 32x32 pixel color image, which will be classified into one of the 10 output classes. This example model implementation needs 32.3 KB to store weights, 40 KB for activations and 3.1 KB for storing the <code>im2col</code> data.</dd></dl>
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Neural Network model definition</div></div>
<dl class="section user"><dt>Variables Description:</dt><dd></dd></dl>
<dl class="section user"><dt></dt><dd><ul>
<li><code>conv1_wt</code>, <code>conv2_wt</code>, <code>conv3_wt</code> are convolution layer weight matrices </li>
<li><code>conv1_bias</code>, <code>conv2_bias</code>, <code>conv3_bias</code> are convolution layer bias arrays </li>
<li><code>ip1_wt</code>, ip1_bias point to fully-connected layer weights and biases </li>
<li><code>input_data</code> points to the input image data </li>
<li><code>output_data</code> points to the classification output </li>
<li><code>col_buffer</code> is a buffer to store the <code>im2col</code> output </li>
<li><code>scratch_buffer</code> is used to store the activation data (intermediate layer outputs)</li>
</ul>
</dd></dl>
<dl class="section user"><dt>CMSIS DSP Software Library Functions Used:</dt><dd></dd></dl>
<dl class="section user"><dt></dt><dd><ul>
<li><a class="el" href="group__NNConv.html#ga98f2ead67d7cbdf558b0cd8a3b8fc148" title="Q7 version of convolution for RGB image. ">arm_convolve_HWC_q7_RGB()</a></li>
<li><a class="el" href="group__NNConv.html#gae00d3c1285907d59657369fc98bcc83f" title="Fast Q7 convolution function. ">arm_convolve_HWC_q7_fast()</a></li>
<li><a class="el" href="group__Acti.html#ga638e803b4fe00426f401783a6255ca30" title="Q7 RELU function. ">arm_relu_q7()</a></li>
<li><a class="el" href="group__Pooling.html#ga99afcdcc61eaf429ab3ee823702e44ce" title="Q7 max pooling function. ">arm_maxpool_q7_HWC()</a></li>
<li><a class="el" href="group__Pooling.html#gae4a7b07f97ec4313524c9fb9fbcb1f6a" title="Q7 average pooling function. ">arm_avepool_q7_HWC()</a></li>
<li><a class="el" href="group__FC.html#gaf82b71ef472a38f8fc9ac414d9d07e67" title="Q7 opt fully-connected layer function. ">arm_fully_connected_q7_opt()</a></li>
<li><a class="el" href="group__FC.html#ga8b7e0c2e989e8c75f0dc789f3115323d" title="Q7 basic fully-connected layer function. ">arm_fully_connected_q7()</a></li>
</ul>
</dd></dl>
<p><b> Refer </b> <a class="el" href="arm__nnexamples__cifar10_8cpp.html">arm_nnexamples_cifar10.cpp</a></p>
<dl class="section user"><dt>[1] https://github.com/BVLC/caffe</dt><dd></dd></dl>
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