arm_compute v18.05

[platform/upstream/armcl.git] / documentation / architecture.xhtml

diff --git a/documentation/architecture.xhtml b/documentation/architecture.xhtml
index 71700ec..3471586 100644 (file)
--- a/documentation/architecture.xhtml
+++ b/documentation/architecture.xhtml
@@ -40,7 +40,7 @@
  <tr style="height: 56px;">
   <td style="padding-left: 0.5em;">
    <div id="projectname">Compute Library
-   &#160;<span id="projectnumber">18.03</span>
+   &#160;<span id="projectnumber">18.05</span>
    </div>
   </td>
  </tr>
@@ -179,7 +179,7 @@ Kernels</h2>
 <div class="fragment"><div class="line"><span class="comment">//Create a kernel object:</span></div><div class="line">MyKernel kernel;</div><div class="line"><span class="comment">// Initialize the kernel with the input/output and options you want to use:</span></div><div class="line">kernel.configure( input, output, option0, option1);</div><div class="line"><span class="comment">// Retrieve the execution window of the kernel:</span></div><div class="line"><span class="keyword">const</span> Window&amp; max_window = kernel.window();</div><div class="line"><span class="comment">// Run the whole kernel in the current thread:</span></div><div class="line">kernel.run( max_window ); <span class="comment">// Run the kernel on the full window</span></div></div><!-- fragment --><h2><a class="anchor" id="S4_2_3"></a>
 Multi-threading</h2>
 <p>The previous section shows how to run a NEON / CPP kernel in the current thread, however if your system has several CPU cores, you will probably want the kernel to use several cores. Here is how this can be done:</p>
-<div class="fragment"><div class="line">    ThreadInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>;</div><div class="line">    info.cpu_info = _info;</div><div class="line"></div><div class="line">    <span class="keyword">const</span> Window      &amp;max_window     = kernel-&gt;window();</div><div class="line">    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_iterations = max_window.num_iterations(split_dimension);</div><div class="line">    info.num_threads                  = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#aabcf39e3917f842dbc5fbb0d802f24d5">std::min</a>(num_iterations, _num_threads);</div><div class="line"></div><div class="line">    <span class="keywordflow">if</span>(num_iterations == 0)</div><div class="line">    {</div><div class="line">        <span class="keywordflow">return</span>;</div><div class="line">    }</div><div class="line"></div><div class="line">    <span class="keywordflow">if</span>(!kernel-&gt;is_parallelisable() || info.num_threads == 1)</div><div class="line">    {</div><div class="line">        kernel-&gt;run(max_window, info);</div><div class="line">    }</div><div class="line">    <span class="keywordflow">else</span></div><div class="line">    {</div><div class="line">        <span class="keywordtype">int</span>  t         = 0;</div><div class="line">        <span class="keyword">auto</span> thread_it = _threads.begin();</div><div class="line"></div><div class="line">        <span class="keywordflow">for</span>(; t &lt; info.num_threads - 1; ++t, ++thread_it)</div><div class="line">        {</div><div class="line">            Window win     = max_window.split_window(split_dimension, t, info.num_threads);</div><div class="line">            info.thread_id = t;</div><div class="line">            thread_it-&gt;start(kernel, win, info);</div><div class="line">        }</div><div class="line"></div><div class="line">        <span class="comment">// Run last part on main thread</span></div><div class="line">        Window win     = max_window.split_window(split_dimension, t, info.num_threads);</div><div class="line">        info.thread_id = t;</div><div class="line">        kernel-&gt;run(win, info);</div><div class="line"></div><div class="line">        <span class="keywordflow">try</span></div><div class="line">        {</div><div class="line">            <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;thread : _threads)</div><div class="line">            {</div><div class="line">                thread.wait();</div><div class="line">            }</div><div class="line">        }</div><div class="line">        <span class="keywordflow">catch</span>(<span class="keyword">const</span> std::system_error &amp;e)</div><div class="line">        {</div><div class="line">            std::cerr &lt;&lt; <span class="stringliteral">&quot;Caught system_error with code &quot;</span> &lt;&lt; e.code() &lt;&lt; <span class="stringliteral">&quot; meaning &quot;</span> &lt;&lt; e.what() &lt;&lt; <span class="charliteral">&#39;\n&#39;</span>;</div><div class="line">        }</div><div class="line">    }</div></div><!-- fragment --><p> This is the very basic implementation used in the NEON runtime library by all the NEON functions.</p>
+<div class="fragment"><div class="line">    ThreadInfo <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a096668313a9a819d54a2e65ec21ff0cc">info</a>;</div><div class="line">    info.cpu_info = &amp;_cpu_info;</div><div class="line"></div><div class="line">    <span class="keyword">const</span> Window      &amp;max_window     = kernel-&gt;window();</div><div class="line">    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_iterations = max_window.num_iterations(split_dimension);</div><div class="line">    info.num_threads                  = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#aabcf39e3917f842dbc5fbb0d802f24d5">std::min</a>(num_iterations, _num_threads);</div><div class="line"></div><div class="line">    <span class="keywordflow">if</span>(num_iterations == 0)</div><div class="line">    {</div><div class="line">        <span class="keywordflow">return</span>;</div><div class="line">    }</div><div class="line"></div><div class="line">    <span class="keywordflow">if</span>(!kernel-&gt;is_parallelisable() || info.num_threads == 1)</div><div class="line">    {</div><div class="line">        kernel-&gt;run(max_window, info);</div><div class="line">    }</div><div class="line">    <span class="keywordflow">else</span></div><div class="line">    {</div><div class="line">        <span class="keywordtype">int</span>  t         = 0;</div><div class="line">        <span class="keyword">auto</span> thread_it = _threads.begin();</div><div class="line"></div><div class="line">        <span class="keywordflow">for</span>(; t &lt; info.num_threads - 1; ++t, ++thread_it)</div><div class="line">        {</div><div class="line">            Window win     = max_window.split_window(split_dimension, t, info.num_threads);</div><div class="line">            info.thread_id = t;</div><div class="line">            thread_it-&gt;start(kernel, win, info);</div><div class="line">        }</div><div class="line"></div><div class="line">        <span class="comment">// Run last part on main thread</span></div><div class="line">        Window win     = max_window.split_window(split_dimension, t, info.num_threads);</div><div class="line">        info.thread_id = t;</div><div class="line">        kernel-&gt;run(win, info);</div><div class="line"></div><div class="line">        <span class="keywordflow">try</span></div><div class="line">        {</div><div class="line">            <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;thread : _threads)</div><div class="line">            {</div><div class="line">                thread.wait();</div><div class="line">            }</div><div class="line">        }</div><div class="line">        <span class="keywordflow">catch</span>(<span class="keyword">const</span> std::system_error &amp;e)</div><div class="line">        {</div><div class="line">            std::cerr &lt;&lt; <span class="stringliteral">&quot;Caught system_error with code &quot;</span> &lt;&lt; e.code() &lt;&lt; <span class="stringliteral">&quot; meaning &quot;</span> &lt;&lt; e.what() &lt;&lt; <span class="charliteral">&#39;\n&#39;</span>;</div><div class="line">        }</div><div class="line">    }</div></div><!-- fragment --><p> This is the very basic implementation used in the NEON runtime library by all the NEON functions.</p>
 <dl class="section see"><dt>See also</dt><dd><a class="el" href="classarm__compute_1_1_c_p_p_scheduler.xhtml" title="C++11 implementation of a pool of threads to automatically split a kernel&#39;s execution among several t...">CPPScheduler</a>.</dd></dl>
 <dl class="section note"><dt>Note</dt><dd>Some kernels like for example <a class="el" href="classarm__compute_1_1_n_e_histogram_kernel.xhtml">NEHistogramKernel</a> need some local temporary buffer to perform their calculations. In order to avoid memory corruption between threads, the local buffer must be of size: <code>memory_needed_per_thread * num_threads</code> and a unique thread_id between 0 and num_threads must be assigned to the <a class="el" href="structarm__compute_1_1_thread_info.xhtml">ThreadInfo</a> object passed to the <code>run</code> function.</dd></dl>
 <h2><a class="anchor" id="S4_2_4"></a>
@@ -200,10 +200,10 @@ Make sure the scheduler's target is not changed after function classes are creat
 <h2><a class="anchor" id="S4_4_2_events_sync"></a>
 OpenCL events and synchronization</h2>
 <p>In order to block until all the jobs in the <a class="el" href="classarm__compute_1_1_c_l_scheduler.xhtml" title="Provides global access to a CL context and command queue. ">CLScheduler</a>'s command queue are done executing the user can call <a class="el" href="classarm__compute_1_1_c_l_scheduler.xhtml#ad55f80ed3cd8b6c4f247763b747016af">CLScheduler::sync()</a> or create a sync event using <a class="el" href="classarm__compute_1_1_c_l_scheduler.xhtml#a6096d26e8e29e1a7f01b0f083fb7e33f">CLScheduler::enqueue_sync_event()</a></p>
-<p>For example: </p><div class="fragment"><div class="line">        PPMLoader     ppm;</div><div class="line">        constexpr <span class="keywordtype">int</span> scale_factor = 2;</div><div class="line"></div><div class="line">        <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a60f9a6836b628a7171914c4afe43b4a7">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a46ecf9ef0fe80ba2ed35acfc29856b7d">default_init</a>();</div><div class="line"></div><div class="line">        <span class="keywordflow">if</span>(argc &lt; 2)</div><div class="line">        {</div><div class="line">            <span class="comment">// Print help</span></div><div class="line">            std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: ./build/cl_events [input_image.ppm]\n\n&quot;</span>;</div><div class="line">            std::cout &lt;&lt; <span class="stringliteral">&quot;No input_image provided, creating a dummy 640x480 image\n&quot;</span>;</div><div class="line">            <span class="comment">// Create an empty grayscale 640x480 image</span></div><div class="line">            <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.allocator()-&gt;init(TensorInfo(640, 480, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>));</div><div class="line">        }</div><div class="line">        <span class="keywordflow">else</span></div><div class="line">        {</div><div class="line">            ppm.open(argv[1]);</div><div class="line">            ppm.init_image(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>);</div><div class="line">        }</div><div class="line"></div><div class="line">        TensorInfo dst_info(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.info()-&gt;dimension(0) / scale_factor, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.info()-&gt;dimension(1) / scale_factor, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>);</div><div class="line"></div><div class="line">        <span class="comment">// Configure the temporary and destination images</span></div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad6dc6b773780dd6b1ad17fc82368d9f3">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa72161e0e3c0f6b2da20f835de6af680">init</a>(dst_info);</div><div class="line">        tmp_scale_median.allocator()-&gt;init(dst_info);</div><div class="line">        tmp_median_gauss.allocator()-&gt;init(dst_info);</div><div class="line"></div><div class="line">        <span class="comment">//Configure the functions:</span></div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acec6d8ad52a28972fa74e071c1a63b6a">scale</a>.configure(&amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, &amp;tmp_scale_median, <a class="code" href="namespacearm__compute.xhtml#a966a9c417ce5e94dca08d9b5e745c0c9a7f5ccbc3d30c2cd3fd04d567946cbde2">InterpolationPolicy::NEAREST_NEIGHBOR</a>, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a4ef59320fbe90fe47d40f1f71e4c5daa">BorderMode::REPLICATE</a>);</div><div class="line">        median.configure(&amp;tmp_scale_median, &amp;tmp_median_gauss, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a4ef59320fbe90fe47d40f1f71e4c5daa">BorderMode::REPLICATE</a>);</div><div class="line">        gauss.configure(&amp;tmp_median_gauss, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a4ef59320fbe90fe47d40f1f71e4c5daa">BorderMode::REPLICATE</a>);</div><div class="line"></div><div class="line">        <span class="comment">// Allocate all the images</span></div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.allocator()-&gt;allocate();</div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad6dc6b773780dd6b1ad17fc82368d9f3">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">allocate</a>();</div><div class="line">        tmp_scale_median.allocator()-&gt;allocate();</div><div class="line">        tmp_median_gauss.allocator()-&gt;allocate();</div><div class="line"></div><div class="line">        <span class="comment">// Fill the input image with the content of the PPM image if a filename was provided:</span></div><div class="line">        <span class="keywordflow">if</span>(ppm.is_open())</div><div class="line">        {</div><div class="line">            ppm.fill_image(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>);</div><div class="line">            output_filename = std::string(argv[1]) + <span class="stringliteral">&quot;_out.ppm&quot;</span>;</div><div class="line">        }</div></div><!-- fragment --> <h2><a class="anchor" id="S4_4_2_cl_neon"></a>
+<p>For example: </p><div class="fragment"><div class="line">        PPMLoader     ppm;</div><div class="line">        constexpr <span class="keywordtype">int</span> scale_factor = 2;</div><div class="line"></div><div class="line">        <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a60f9a6836b628a7171914c4afe43b4a7">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a46ecf9ef0fe80ba2ed35acfc29856b7d">default_init</a>();</div><div class="line"></div><div class="line">        <span class="keywordflow">if</span>(argc &lt; 2)</div><div class="line">        {</div><div class="line">            <span class="comment">// Print help</span></div><div class="line">            std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: ./build/cl_events [input_image.ppm]\n\n&quot;</span>;</div><div class="line">            std::cout &lt;&lt; <span class="stringliteral">&quot;No input_image provided, creating a dummy 640x480 image\n&quot;</span>;</div><div class="line">            <span class="comment">// Create an empty grayscale 640x480 image</span></div><div class="line">            <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.allocator()-&gt;init(TensorInfo(640, 480, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>));</div><div class="line">        }</div><div class="line">        <span class="keywordflow">else</span></div><div class="line">        {</div><div class="line">            ppm.open(argv[1]);</div><div class="line">            ppm.init_image(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>);</div><div class="line">        }</div><div class="line"></div><div class="line">        TensorInfo dst_info(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.info()-&gt;dimension(0) / scale_factor, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.info()-&gt;dimension(1) / scale_factor, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>);</div><div class="line"></div><div class="line">        <span class="comment">// Configure the temporary and destination images</span></div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a256b18d4e6fdbbff14937b4b9089bdd3">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa72161e0e3c0f6b2da20f835de6af680">init</a>(dst_info);</div><div class="line">        tmp_scale_median.allocator()-&gt;init(dst_info);</div><div class="line">        tmp_median_gauss.allocator()-&gt;init(dst_info);</div><div class="line"></div><div class="line">        <span class="comment">//Configure the functions:</span></div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acec6d8ad52a28972fa74e071c1a63b6a">scale</a>.configure(&amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, &amp;tmp_scale_median, <a class="code" href="namespacearm__compute.xhtml#a966a9c417ce5e94dca08d9b5e745c0c9a7f5ccbc3d30c2cd3fd04d567946cbde2">InterpolationPolicy::NEAREST_NEIGHBOR</a>, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a4ef59320fbe90fe47d40f1f71e4c5daa">BorderMode::REPLICATE</a>);</div><div class="line">        median.configure(&amp;tmp_scale_median, &amp;tmp_median_gauss, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a4ef59320fbe90fe47d40f1f71e4c5daa">BorderMode::REPLICATE</a>);</div><div class="line">        gauss.configure(&amp;tmp_median_gauss, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a4ef59320fbe90fe47d40f1f71e4c5daa">BorderMode::REPLICATE</a>);</div><div class="line"></div><div class="line">        <span class="comment">// Allocate all the images</span></div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.allocator()-&gt;allocate();</div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a256b18d4e6fdbbff14937b4b9089bdd3">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line">        tmp_scale_median.allocator()-&gt;allocate();</div><div class="line">        tmp_median_gauss.allocator()-&gt;allocate();</div><div class="line"></div><div class="line">        <span class="comment">// Fill the input image with the content of the PPM image if a filename was provided:</span></div><div class="line">        <span class="keywordflow">if</span>(ppm.is_open())</div><div class="line">        {</div><div class="line">            ppm.fill_image(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>);</div><div class="line">            output_filename = std::string(argv[1]) + <span class="stringliteral">&quot;_out.ppm&quot;</span>;</div><div class="line">        }</div></div><!-- fragment --> <h2><a class="anchor" id="S4_4_2_cl_neon"></a>
 OpenCL / NEON interoperability</h2>
 <p>You can mix OpenCL and NEON kernels and functions. However it is the user's responsibility to handle the mapping/unmapping of OpenCL objects, for example:</p>
-<div class="fragment"><div class="line">        PPMLoader ppm;</div><div class="line"></div><div class="line">        <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a60f9a6836b628a7171914c4afe43b4a7">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a46ecf9ef0fe80ba2ed35acfc29856b7d">default_init</a>();</div><div class="line"></div><div class="line">        <span class="keywordflow">if</span>(argc &lt; 2)</div><div class="line">        {</div><div class="line">            <span class="comment">// Print help</span></div><div class="line">            std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: ./build/cl_convolution [input_image.ppm]\n\n&quot;</span>;</div><div class="line">            std::cout &lt;&lt; <span class="stringliteral">&quot;No input_image provided, creating a dummy 640x480 image\n&quot;</span>;</div><div class="line">            <span class="comment">// Create an empty grayscale 640x480 image</span></div><div class="line">            <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.allocator()-&gt;init(TensorInfo(640, 480, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>));</div><div class="line">        }</div><div class="line">        <span class="keywordflow">else</span></div><div class="line">        {</div><div class="line">            ppm.open(argv[1]);</div><div class="line">            ppm.init_image(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>);</div><div class="line">        }</div><div class="line"></div><div class="line">        TensorInfo scale_median_info(TensorInfo(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.info()-&gt;dimension(0) / 2, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.info()-&gt;dimension(1) / 2, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>));</div><div class="line"></div><div class="line">        <span class="comment">// Configure the temporary and destination images</span></div><div class="line">        scale_median.allocator()-&gt;init(scale_median_info);</div><div class="line">        median_gauss.allocator()-&gt;init(scale_median_info);</div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad6dc6b773780dd6b1ad17fc82368d9f3">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa72161e0e3c0f6b2da20f835de6af680">init</a>(scale_median_info);</div><div class="line"></div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acec6d8ad52a28972fa74e071c1a63b6a">scale</a>.configure(&amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, &amp;scale_median, <a class="code" href="namespacearm__compute.xhtml#a966a9c417ce5e94dca08d9b5e745c0c9a7f5ccbc3d30c2cd3fd04d567946cbde2">InterpolationPolicy::NEAREST_NEIGHBOR</a>, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a4ef59320fbe90fe47d40f1f71e4c5daa">BorderMode::REPLICATE</a>);</div><div class="line">        median.configure(&amp;scale_median, &amp;median_gauss, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a4ef59320fbe90fe47d40f1f71e4c5daa">BorderMode::REPLICATE</a>);</div><div class="line">        gauss.configure(&amp;median_gauss, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a4ef59320fbe90fe47d40f1f71e4c5daa">BorderMode::REPLICATE</a>);</div><div class="line"></div><div class="line">        <span class="comment">// Allocate all the images</span></div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.allocator()-&gt;allocate();</div><div class="line">        scale_median.allocator()-&gt;allocate();</div><div class="line">        median_gauss.allocator()-&gt;allocate();</div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad6dc6b773780dd6b1ad17fc82368d9f3">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">allocate</a>();</div><div class="line"></div><div class="line">        <span class="comment">// Fill the input image with the content of the PPM image if a filename was provided:</span></div><div class="line">        <span class="keywordflow">if</span>(ppm.is_open())</div><div class="line">        {</div><div class="line">            ppm.fill_image(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>);</div><div class="line">            <span class="keyword">const</span> std::string output_filename = std::string(argv[1]) + <span class="stringliteral">&quot;_out.ppm&quot;</span>;</div><div class="line">        }</div></div><!-- fragment --> <dl class="section see"><dt>See also</dt><dd>main_neoncl_scale_median_gaussian</dd></dl>
+<div class="fragment"><div class="line">        PPMLoader ppm;</div><div class="line"></div><div class="line">        <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a60f9a6836b628a7171914c4afe43b4a7">CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a46ecf9ef0fe80ba2ed35acfc29856b7d">default_init</a>();</div><div class="line"></div><div class="line">        <span class="keywordflow">if</span>(argc &lt; 2)</div><div class="line">        {</div><div class="line">            <span class="comment">// Print help</span></div><div class="line">            std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: ./build/cl_convolution [input_image.ppm]\n\n&quot;</span>;</div><div class="line">            std::cout &lt;&lt; <span class="stringliteral">&quot;No input_image provided, creating a dummy 640x480 image\n&quot;</span>;</div><div class="line">            <span class="comment">// Create an empty grayscale 640x480 image</span></div><div class="line">            <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.allocator()-&gt;init(TensorInfo(640, 480, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>));</div><div class="line">        }</div><div class="line">        <span class="keywordflow">else</span></div><div class="line">        {</div><div class="line">            ppm.open(argv[1]);</div><div class="line">            ppm.init_image(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>);</div><div class="line">        }</div><div class="line"></div><div class="line">        TensorInfo scale_median_info(TensorInfo(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.info()-&gt;dimension(0) / 2, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.info()-&gt;dimension(1) / 2, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>));</div><div class="line"></div><div class="line">        <span class="comment">// Configure the temporary and destination images</span></div><div class="line">        scale_median.allocator()-&gt;init(scale_median_info);</div><div class="line">        median_gauss.allocator()-&gt;init(scale_median_info);</div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a256b18d4e6fdbbff14937b4b9089bdd3">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa72161e0e3c0f6b2da20f835de6af680">init</a>(scale_median_info);</div><div class="line"></div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acec6d8ad52a28972fa74e071c1a63b6a">scale</a>.configure(&amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, &amp;scale_median, <a class="code" href="namespacearm__compute.xhtml#a966a9c417ce5e94dca08d9b5e745c0c9a7f5ccbc3d30c2cd3fd04d567946cbde2">InterpolationPolicy::NEAREST_NEIGHBOR</a>, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a4ef59320fbe90fe47d40f1f71e4c5daa">BorderMode::REPLICATE</a>);</div><div class="line">        median.configure(&amp;scale_median, &amp;median_gauss, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a4ef59320fbe90fe47d40f1f71e4c5daa">BorderMode::REPLICATE</a>);</div><div class="line">        gauss.configure(&amp;median_gauss, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a4ef59320fbe90fe47d40f1f71e4c5daa">BorderMode::REPLICATE</a>);</div><div class="line"></div><div class="line">        <span class="comment">// Allocate all the images</span></div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.allocator()-&gt;allocate();</div><div class="line">        scale_median.allocator()-&gt;allocate();</div><div class="line">        median_gauss.allocator()-&gt;allocate();</div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a256b18d4e6fdbbff14937b4b9089bdd3">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"></div><div class="line">        <span class="comment">// Fill the input image with the content of the PPM image if a filename was provided:</span></div><div class="line">        <span class="keywordflow">if</span>(ppm.is_open())</div><div class="line">        {</div><div class="line">            ppm.fill_image(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>);</div><div class="line">            <span class="keyword">const</span> std::string output_filename = std::string(argv[1]) + <span class="stringliteral">&quot;_out.ppm&quot;</span>;</div><div class="line">        }</div></div><!-- fragment --> <dl class="section see"><dt>See also</dt><dd>main_neoncl_scale_median_gaussian</dd></dl>
 <h1><a class="anchor" id="S4_5_algorithms"></a>
 Algorithms</h1>
 <p>All computer vision algorithms in this library have been implemented following the <a href="https://www.khronos.org/registry/vx/specs/1.1/html/">OpenVX 1.1 specifications</a>. Please refer to the Khronos documentation for more information.</p>
@@ -227,7 +227,7 @@ Padding</h3>
 <ul>
 <li>Accurate padding:</li>
 </ul>
-<div class="fragment"><div class="line">        PPMLoader ppm;</div><div class="line"></div><div class="line">        <span class="keywordflow">if</span>(argc &lt; 2)</div><div class="line">        {</div><div class="line">            <span class="comment">// Print help</span></div><div class="line">            std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: ./build/neon_convolution [input_image.ppm]\n\n&quot;</span>;</div><div class="line">            std::cout &lt;&lt; <span class="stringliteral">&quot;No input_image provided, creating a dummy 640x480 image\n&quot;</span>;</div><div class="line">            <span class="comment">// Initialize just the dimensions and format of your buffers:</span></div><div class="line">            <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.allocator()-&gt;init(TensorInfo(640, 480, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>));</div><div class="line">        }</div><div class="line">        <span class="keywordflow">else</span></div><div class="line">        {</div><div class="line">            ppm.open(argv[1]);</div><div class="line">            <span class="comment">// Initialize just the dimensions and format of your buffers:</span></div><div class="line">            ppm.init_image(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>);</div><div class="line">        }</div><div class="line"></div><div class="line">        <span class="comment">// Initialize just the dimensions and format of the temporary and destination images:</span></div><div class="line">        tmp.allocator()-&gt;init(*<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.info());</div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad6dc6b773780dd6b1ad17fc82368d9f3">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa72161e0e3c0f6b2da20f835de6af680">init</a>(*<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.info());</div><div class="line"></div><div class="line">        <span class="comment">// Apply a Gaussian 3x3 filter to the source image followed by a Gaussian 5x5:</span></div><div class="line">        <span class="comment">// The function will automatically update the padding information inside input and output to match its requirements</span></div><div class="line">        conv3x3.configure(&amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, &amp;tmp, <a class="code" href="cl__convolution_8cpp.xhtml#a741ba5321da40184f8653e0a50ace070">gaussian3x3</a>, 0 <span class="comment">/* Let arm_compute calculate the scale */</span>, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a0db45d2a4141101bdfe48e3314cfbca3">BorderMode::UNDEFINED</a>);</div><div class="line">        conv5x5.configure(&amp;tmp, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, <a class="code" href="cl__convolution_8cpp.xhtml#a565013cf7e49a591bacd548571951f94">gaussian5x5</a>, 0 <span class="comment">/* Let arm_compute calculate the scale */</span>, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a0db45d2a4141101bdfe48e3314cfbca3">BorderMode::UNDEFINED</a>);</div><div class="line"></div><div class="line">        <span class="comment">// Now that the padding requirements are known we can allocate the images:</span></div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.allocator()-&gt;allocate();</div><div class="line">        tmp.allocator()-&gt;allocate();</div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#ad6dc6b773780dd6b1ad17fc82368d9f3">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa8a4946cd749d482dd996874d295af85">allocate</a>();</div><div class="line"></div><div class="line">        <span class="comment">// Fill the input image with the content of the PPM image if a filename was provided:</span></div><div class="line">        <span class="keywordflow">if</span>(ppm.is_open())</div><div class="line">        {</div><div class="line">            ppm.fill_image(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>);</div><div class="line">            output_filename = std::string(argv[1]) + <span class="stringliteral">&quot;_out.ppm&quot;</span>;</div><div class="line">        }</div></div><!-- fragment --> <dl class="section note"><dt>Note</dt><dd>It's important to call allocate <b>after</b> the function is configured: if the image / tensor is already allocated then the function will shrink its execution window instead of increasing the padding. (See below for more details).</dd></dl>
+<div class="fragment"><div class="line">        PPMLoader ppm;</div><div class="line"></div><div class="line">        <span class="keywordflow">if</span>(argc &lt; 2)</div><div class="line">        {</div><div class="line">            <span class="comment">// Print help</span></div><div class="line">            std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: ./build/neon_convolution [input_image.ppm]\n\n&quot;</span>;</div><div class="line">            std::cout &lt;&lt; <span class="stringliteral">&quot;No input_image provided, creating a dummy 640x480 image\n&quot;</span>;</div><div class="line">            <span class="comment">// Initialize just the dimensions and format of your buffers:</span></div><div class="line">            <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.allocator()-&gt;init(TensorInfo(640, 480, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>));</div><div class="line">        }</div><div class="line">        <span class="keywordflow">else</span></div><div class="line">        {</div><div class="line">            ppm.open(argv[1]);</div><div class="line">            <span class="comment">// Initialize just the dimensions and format of your buffers:</span></div><div class="line">            ppm.init_image(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>);</div><div class="line">        }</div><div class="line"></div><div class="line">        <span class="comment">// Initialize just the dimensions and format of the temporary and destination images:</span></div><div class="line">        tmp.allocator()-&gt;init(*<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.info());</div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a256b18d4e6fdbbff14937b4b9089bdd3">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_i_tensor_allocator.xhtml#aa72161e0e3c0f6b2da20f835de6af680">init</a>(*<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.info());</div><div class="line"></div><div class="line">        <span class="comment">// Apply a Gaussian 3x3 filter to the source image followed by a Gaussian 5x5:</span></div><div class="line">        <span class="comment">// The function will automatically update the padding information inside input and output to match its requirements</span></div><div class="line">        conv3x3.configure(&amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>, &amp;tmp, <a class="code" href="cl__convolution_8cpp.xhtml#a741ba5321da40184f8653e0a50ace070">gaussian3x3</a>, 0 <span class="comment">/* Let arm_compute calculate the scale */</span>, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a0db45d2a4141101bdfe48e3314cfbca3">BorderMode::UNDEFINED</a>);</div><div class="line">        conv5x5.configure(&amp;tmp, &amp;<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>, <a class="code" href="cl__convolution_8cpp.xhtml#a565013cf7e49a591bacd548571951f94">gaussian5x5</a>, 0 <span class="comment">/* Let arm_compute calculate the scale */</span>, <a class="code" href="namespacearm__compute.xhtml#a15a05537a472ee742404821851529327a0db45d2a4141101bdfe48e3314cfbca3">BorderMode::UNDEFINED</a>);</div><div class="line"></div><div class="line">        <span class="comment">// Now that the padding requirements are known we can allocate the images:</span></div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>.allocator()-&gt;allocate();</div><div class="line">        tmp.allocator()-&gt;allocate();</div><div class="line">        <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.<a class="code" href="classarm__compute_1_1_c_l_tensor.xhtml#a256b18d4e6fdbbff14937b4b9089bdd3">allocator</a>()-&gt;<a class="code" href="classarm__compute_1_1_c_l_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"></div><div class="line">        <span class="comment">// Fill the input image with the content of the PPM image if a filename was provided:</span></div><div class="line">        <span class="keywordflow">if</span>(ppm.is_open())</div><div class="line">        {</div><div class="line">            ppm.fill_image(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>);</div><div class="line">            output_filename = std::string(argv[1]) + <span class="stringliteral">&quot;_out.ppm&quot;</span>;</div><div class="line">        }</div></div><!-- fragment --> <dl class="section note"><dt>Note</dt><dd>It's important to call allocate <b>after</b> the function is configured: if the image / tensor is already allocated then the function will shrink its execution window instead of increasing the padding. (See below for more details).</dd></dl>
 <ul>
 <li>Manual padding / no padding / auto padding: You can allocate your images / tensors up front (before configuring your functions). In that case the function will use whatever padding is available and will shrink its execution window if there isn't enough padding available (which translates into a smaller valid region for the output). See also <a class="el" href="architecture.xhtml#valid_region">Valid regions</a>). If you don't want to manually set the padding but still want to allocate your objects upfront then you can use auto_padding. It guarantees that the allocation will have enough padding to run any of the provided functions.</li>
 </ul>
@@ -252,7 +252,7 @@ Images and Tensors description conventions</h2>
 Unless specified otherwise in the kernel's or function's documentation the number of channels for tensors is expected to be 1 (For images, the number of channels is inferred from the <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a>).</dd></dl>
 <dl class="section attention"><dt>Attention</dt><dd>Regardless of the <a class="el" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> used by a tensor the <a class="el" href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">ITensor::buffer()</a> method will always return a uint8_t pointer, and all the metadata in <a class="el" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> will be expressed in bytes. It is the user's responsibility to cast the pointer to the correct type.</dd></dl>
 <p>For example, to read the element located at the coordinates (x,y) of a float tensor:</p>
-<div class="fragment"><div class="line"><span class="keywordtype">float</span> <a class="code" href="hwc_8hpp.xhtml#a0f61d63b009d0880a89c843bd50d8d76">value</a> = *<span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">float</span>*<span class="keyword">&gt;</span>(input.buffer() + input.info()-&gt;offset_element_in_bytes(Coordinates(x,y)));</div></div><!-- fragment --><h2><a class="anchor" id="S4_6_4_working_with_objects"></a>
+<div class="fragment"><div class="line"><span class="keywordtype">float</span> value = *<span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">float</span>*<span class="keyword">&gt;</span>(input.buffer() + input.info()-&gt;offset_element_in_bytes(Coordinates(x,y)));</div></div><!-- fragment --><h2><a class="anchor" id="S4_6_4_working_with_objects"></a>
 Working with Images and Tensors using iterators</h2>
 <p>The library provides some iterators to access objects' data. Iterators are created by associating a data object (An image or a tensor for example) with an iteration window.</p>
 <p>Iteration windows are defined by an array of dimensions, each of which consists of a start, end and step.</p>
@@ -311,7 +311,7 @@ OpenCL Tuner</h1>
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     <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.11 </li>
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