<?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 Re: ONNX support on i.MX95 NPU in i.MX Processors</title>
    <link>https://community.nxp.com/t5/i-MX-Processors/ONNX-support-on-i-MX95-NPU/m-p/2151382#M240071</link>
    <description>&lt;P&gt;Okey, I see. A bit misleading to put it in the machine learning guide then. This means that no ONNX runtime models are supported on the NPU as of yet i would presume?&amp;nbsp;&lt;/P&gt;</description>
    <pubDate>Wed, 13 Aug 2025 06:11:08 GMT</pubDate>
    <dc:creator>1o_o1</dc:creator>
    <dc:date>2025-08-13T06:11:08Z</dc:date>
    <item>
      <title>ONNX support on i.MX95 NPU</title>
      <link>https://community.nxp.com/t5/i-MX-Processors/ONNX-support-on-i-MX95-NPU/m-p/2146028#M239809</link>
      <description>&lt;P&gt;Quantized onnx models is supposedly supported on the NPU on imx95 as of the latest release (LF6.12.20_2.0.0). Running both int8 and int4 onnx versions of Gemma3-1B on CPU provides expected results, while the NPU produces nothing but nonsense. Running quantized tflite models on the NPU on imx95 board requires conversion by the neutron converter specifying imx95 as a target. Does quantized onnx models need a conversion step before being runable on the NPU?&amp;nbsp;The converter does not seem to support onnx.&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Mon, 04 Aug 2025 08:08:18 GMT</pubDate>
      <guid>https://community.nxp.com/t5/i-MX-Processors/ONNX-support-on-i-MX95-NPU/m-p/2146028#M239809</guid>
      <dc:creator>1o_o1</dc:creator>
      <dc:date>2025-08-04T08:08:18Z</dc:date>
    </item>
    <item>
      <title>Re: ONNX support on i.MX95 NPU</title>
      <link>https://community.nxp.com/t5/i-MX-Processors/ONNX-support-on-i-MX95-NPU/m-p/2150411#M240041</link>
      <description>&lt;P&gt;Hi&amp;nbsp;&lt;a href="https://community.nxp.com/t5/user/viewprofilepage/user-id/253088"&gt;@1o_o1&lt;/a&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Currently ONNX LLMs is not supported on Nertron NPU now.&amp;nbsp; We will support the ONNX Runtime for LLMs in Q3 BSP release.&amp;nbsp; Then you just need to specify the Neutron provider in the ONNX runtime API to deploy the supported Ops in LLM on Neutron NPU.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Regards&lt;/P&gt;
&lt;P&gt;Daniel&lt;/P&gt;</description>
      <pubDate>Tue, 12 Aug 2025 02:05:31 GMT</pubDate>
      <guid>https://community.nxp.com/t5/i-MX-Processors/ONNX-support-on-i-MX95-NPU/m-p/2150411#M240041</guid>
      <dc:creator>danielchen</dc:creator>
      <dc:date>2025-08-12T02:05:31Z</dc:date>
    </item>
    <item>
      <title>Re: ONNX support on i.MX95 NPU</title>
      <link>https://community.nxp.com/t5/i-MX-Processors/ONNX-support-on-i-MX95-NPU/m-p/2151382#M240071</link>
      <description>&lt;P&gt;Okey, I see. A bit misleading to put it in the machine learning guide then. This means that no ONNX runtime models are supported on the NPU as of yet i would presume?&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Wed, 13 Aug 2025 06:11:08 GMT</pubDate>
      <guid>https://community.nxp.com/t5/i-MX-Processors/ONNX-support-on-i-MX95-NPU/m-p/2151382#M240071</guid>
      <dc:creator>1o_o1</dc:creator>
      <dc:date>2025-08-13T06:11:08Z</dc:date>
    </item>
  </channel>
</rss>

