<p>From <a href="https://arxiv.org/abs/1903.08469v1" rel="noreferrer">https://arxiv.org/abs/1903.08469v1</a> :</p>

<p>"However, MobileNet V2 uses <strong>depthwise separable convolutions which are not directly supported in GPU firmware</strong> (the cuDNN library). Therefore, MobileNet V2 tends to be slower than ResNet18 in most experimental setups. Note that the same issue disqualifies usage of the DenseNet architecture [12], since it requires efficient convolution over a non-contiguous tensor, which is still not supported in cuDNN."</p>
Why the MobileNetV2 is faster than MobileNetV1 only at mobile device?
<p>I am studying about Google's brandnew <code>MobileNetV2</code> architecture.</p>

<p>During studying, I've read this string at Tensorflow model zoo Github</p>

<p>'For example Mobilenet V2 is faster on mobile devices than Mobilenet V1, but is slightly slower on desktop GPU.'</p>

<p>So, my question is,</p>

<p>How that could be possible? I really want to know why.</p>