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<div id="projectname">CMSIS-NN
&#160;<span id="projectnumber">Version 1.2.0</span>
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<div id="projectbrief">CMSIS NN Software Library</div>
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<div class="title">CMSIS NN Software Library </div> </div>
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<div class="textblock"><h2>Introduction </h2>
<p>This user manual describes the CMSIS NN software library, a collection of efficient neural network kernels developed to maximize the performance and minimize the memory footprint of neural networks on Cortex-M processor cores.</p>
<p>The library is divided into a number of functions each covering a specific category:</p>
<ul>
<li>Neural Network Convolution Functions</li>
<li>Neural Network Activation Functions</li>
<li>Fully-connected Layer Functions</li>
<li>Neural Network Pooling Functions</li>
<li>Softmax Functions</li>
<li>Neural Network Support Functions</li>
</ul>
<p>The library has separate functions for operating on different weight and activation data types including 8-bit integers (q7_t) and 16-bit integers (q15_t). The descrition of the kernels are included in the function description. The implementation details are also described in this paper [1].</p>
<h2>Block Diagram </h2>
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<img src="CMSIS-NN-OVERVIEW.PNG" alt="CMSIS-NN-OVERVIEW.PNG"/>
</div>
<h2>Examples </h2>
<p>The library ships with a number of examples which demonstrate how to use the library functions.</p>
<h2>Pre-processor Macros </h2>
<p>Each library project have differant pre-processor macros.</p>
<ul>
<li>ARM_MATH_DSP:</li>
</ul>
<p>Define macro ARM_MATH_DSP, If the silicon supports DSP instructions.</p>
<ul>
<li>ARM_MATH_BIG_ENDIAN:</li>
</ul>
<p>Define macro ARM_MATH_BIG_ENDIAN to build the library for big endian targets. By default library builds for little endian targets.</p>
<ul>
<li>ARM_NN_TRUNCATE:</li>
</ul>
<p>Define macro ARM_NN_TRUNCATE to use floor instead of round-to-the-nearest-int for the computation.</p>
<h2>Copyright Notice </h2>
<p>Copyright (C) 2010-2018 Arm Limited. All rights reserved.</p>
<p>[1] CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs <a href="https://arxiv.org/abs/1801.06601">https://arxiv.org/abs/1801.06601</a> </p>
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