DNN performance on mobile platforms

asked 2017-08-22 06:39:32 -0600

Jaykob
61 ●1 ●5

updated 2017-08-22 06:40:01 -0600

Hi, I'm in the process of deciding whether to run my tensorflow model on iOS and Android using OpenCV's dnn module vs directly with tensorflow. Advantage using OpenCV would be that I don't need an additional lib (tensorflow) as I'm using OpenCV anyway. Disadvantage is that my model doesn't run out of the box due to some not yet supported layer types. But I think that could be solved somehow.

The main criteria would be performance, though. I only read that the dnn module got significantly faster with the 3.3.0 release, however I didn't find any comparison to tensorflow itself. Does anybody have some experience with both methods and could give me some hints?

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answered 2017-08-22 06:57:28 -0600

dkurtaev
21 ●1 ●2

Hi, @Jaykob! Unfortunately, I haven't experimented with DNN on mobile platforms but I can tell you that TensorFlow (+ Eigen computational backend by default) on CPU is definitely faster. In example, Inception-5h in TF takes 17.9ms versus 19.58ms in DNN. On the other hand, you'd like to run your model on GPU (using OpenCL, if it possible) to save power, in example. DNN is going to has some OpenCL backends (libdnn and Halide). I don't know if TensorFlow supports OpenCL. We're interested to know, what kind of layers are still required for you to extend TensorFlow importer as fast as it possible. Please, keep in touch with your decision. May be it will be the first time I run model on mobile device to help you =)

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