<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" version="2.0">
  <channel>
    <title>topic Keras and imx8m Plus GPU/ML module in eIQ Machine Learning Software</title>
    <link>https://community.nxp.com/t5/eIQ-Machine-Learning-Software/Keras-and-imx8m-Plus-GPU-ML-module/m-p/1134039#M278</link>
    <description>&lt;P&gt;Hi,&lt;/P&gt;&lt;P&gt;I'm working with the i.MX 8M Plus EVK to test the ML accelerator integrated in the SoC.&lt;/P&gt;&lt;P&gt;To do that, I've created a Keras model with TensorFlow 1.15. Now I would like to run it on the GPU/ML module of the i.MX 8M Plus.&lt;/P&gt;&lt;P&gt;The eIQ user's guide (rev. L5.4.24_2.1.0) says:&lt;BR /&gt;"The GPU/ML module driver does not support per-channel quantization yet. Therefore post-training quantization of models with TensorFlow v2 cannot be used if the model is supposed to run on the GPU/ML module (inference on CPU does not have this limitation). TensorFlow v1 quantization-aware training and model conversion is recommended in this case".&lt;/P&gt;&lt;P&gt;It seems that Keras quantization-aware training only supports TensorFlow v2 and it is per-channel/per-axis for convolutional layers: &lt;A href="https://www.tensorflow.org/model_optimization/guide/quantization/training#general_support_matrix" target="_blank"&gt;https://www.tensorflow.org/model_optimization/guide/quantization/training#general_support_matrix&lt;/A&gt;.&lt;/P&gt;&lt;P&gt;Is there a way to do per-tensor quantization for a Keras model with convolutional layers and run it on the GPU/ML module? If not, how am I supposed to run my model&amp;nbsp;on the GPU/ML module?&lt;/P&gt;</description>
    <pubDate>Wed, 02 Sep 2020 09:28:01 GMT</pubDate>
    <dc:creator>mpolo</dc:creator>
    <dc:date>2020-09-02T09:28:01Z</dc:date>
    <item>
      <title>Keras and imx8m Plus GPU/ML module</title>
      <link>https://community.nxp.com/t5/eIQ-Machine-Learning-Software/Keras-and-imx8m-Plus-GPU-ML-module/m-p/1134039#M278</link>
      <description>&lt;P&gt;Hi,&lt;/P&gt;&lt;P&gt;I'm working with the i.MX 8M Plus EVK to test the ML accelerator integrated in the SoC.&lt;/P&gt;&lt;P&gt;To do that, I've created a Keras model with TensorFlow 1.15. Now I would like to run it on the GPU/ML module of the i.MX 8M Plus.&lt;/P&gt;&lt;P&gt;The eIQ user's guide (rev. L5.4.24_2.1.0) says:&lt;BR /&gt;"The GPU/ML module driver does not support per-channel quantization yet. Therefore post-training quantization of models with TensorFlow v2 cannot be used if the model is supposed to run on the GPU/ML module (inference on CPU does not have this limitation). TensorFlow v1 quantization-aware training and model conversion is recommended in this case".&lt;/P&gt;&lt;P&gt;It seems that Keras quantization-aware training only supports TensorFlow v2 and it is per-channel/per-axis for convolutional layers: &lt;A href="https://www.tensorflow.org/model_optimization/guide/quantization/training#general_support_matrix" target="_blank"&gt;https://www.tensorflow.org/model_optimization/guide/quantization/training#general_support_matrix&lt;/A&gt;.&lt;/P&gt;&lt;P&gt;Is there a way to do per-tensor quantization for a Keras model with convolutional layers and run it on the GPU/ML module? If not, how am I supposed to run my model&amp;nbsp;on the GPU/ML module?&lt;/P&gt;</description>
      <pubDate>Wed, 02 Sep 2020 09:28:01 GMT</pubDate>
      <guid>https://community.nxp.com/t5/eIQ-Machine-Learning-Software/Keras-and-imx8m-Plus-GPU-ML-module/m-p/1134039#M278</guid>
      <dc:creator>mpolo</dc:creator>
      <dc:date>2020-09-02T09:28:01Z</dc:date>
    </item>
  </channel>
</rss>

