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  46    <div id="projectname">CMSIS-Stream
  47    </div>
  48    <div id="projectbrief">Optimized pipelines for data stream processing</div>
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 123   <div class="headertitle"><div class="title">CMSIS-Stream </div></div>
 124 </div><!--header-->
 125 <div class="contents">
 126 <div class="textblock"><p><a class="anchor" id="mainpage"></a> <b>CMSIS-Stream</b> is a Python package that provides methods, interfaces, and tools to optimize data block streaming between processing steps of a DSP/ML application.</p>
 127 <h1><a class="anchor" id="autotoc_md0"></a>
 128 Access to CMSIS-Stream</h1>
 129 <ul>
 130 <li><a href="https://github.com/ARM-software/cmsis-stream"><b>CMSIS-Stream GitHub Repo</b></a> - provides templates, usage examples, and access to documentation.</li>
 131 <li><a href="https://pypi.org/project/cmsis-stream/"><b>CMSIS-Stream Python package</b></a> - provides easy access to tools via standard Python delivery system.</li>
 132 </ul>
 133 <h1><a class="anchor" id="autotoc_md1"></a>
 134 Benefits</h1>
 135 <ul>
 136 <li>CMSIS-Stream enables a modular design that makes it easier to develop and maintain DSP pipelines.</li>
 137 <li>The provided tools generate at build time an optimized scheduling of the processing nodes with minimized memory overhead.</li>
 138 <li>Creates an easy to understand representation of the design visualized with a compute graph.</li>
 139 </ul>
 140 <h1><a class="anchor" id="autotoc_md2"></a>
 141 Components</h1>
 142 <p>The components of CMSIS-Stream are:</p>
 143 <ul>
 144 <li><b>Compute Graph</b> is the decomposition of the application in a directed graph that shows the data flow between processing nodes. It describes the data formats, FIFO buffers, data streams, and processing steps using a Python script file.</li>
 145 <li><b>Tools</b> convert the Compute Graph with Python into processing steps at build-time with optimized schedule and memory usage.</li>
 146 <li><b>Interfaces</b> header files, templates, and methods for data management (that works also on AMP systems).</li>
 147 <li><b>Usage Examples</b> that help a software developer to get started.</li>
 148 </ul>
 149 <h1><a class="anchor" id="autotoc_md3"></a>
 150 Example Compute Graph</h1>
 151 <p>The diagram below shows a Compute Graph with four nodes:</p>
 152 <ul>
 153 <li>A source generating 5 values in with data type <code>q15</code> each time it is run.</li>
 154 <li>A processing node (Filter) consuming and producing 7 values with data type <code>q15</code> each time it is run.</li>
 155 <li>A processing node (Detector) consuming 5 values with data type <code>q15</code> and producing 2 values with data type <code>f32</code> each it is run.</li>
 156 <li>A sink consuming 10 values with data type <code>f32</code> each time it is run.</li>
 157 </ul>
 158 <div class="image">
 159 <img src="Compute-Graph-Sample.png" alt=""/>
 160 <div class="caption">
 161 Sample Compute Graph</div></div>
 162     <p>This Compute Graph is described with a Python script file that defines the nodes and their connections. This Python script computes a C++ implementation with static schedule and correctly sized memory buffers between each node. Nodes that are implemented in C are integrated using C++ wrappers.</p>
 163 <h1><a class="anchor" id="autotoc_md4"></a>
 164 Complex DSP Pipelines</h1>
 165 <p>CMSIS-Stream gives the developer a toolbox that allows to create optimized DSP pipelines, that are for example required to optimize machine learning (ML) software stacks. With a Compute Graph complex DSP pipelines like shown in the diagram below can be described. By optimizing signal conditioning and feature extraction, the complexity of the ML classifier. More DSP pre-processing helps therefore lowering the overall performance that is required for a ML application.</p>
 166 <div class="image">
 167 <img src="ML-Stack.png" alt=""/>
 168 <div class="caption">
 169 Example Machine Learning Stack</div></div>
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