> A mobile device can execute a pretrained model fine.

Of course it can. The problem is with the size of the network you might want to run.

From your first link, which is about single character level image processing

> We needed to develop a very small neural net, and put severe limits on how much we tried to teach it—in essence, put an upper bound on the density of information it handles.

And from the voice training paper:

> While our server-based model has 50M parameters (k = 4, nh = 2560, ni = 26 and no = 7969), to reduce the memory and computation requirement for the embedded model, we experimented with a variety of sizes and chose k = 6, nh = 512, ni = 16 and no = 2000, or 2.7M parameters

AlexNet is, what, 60M+? VGG is pretty big too.

I guess I wasn't too clear. Yes, there are good results from nets that we can run on mobile devices in realtime. We do, however, want to run significantly larger nets.

[1] is a Google-authored demo running Inception-v3[2] (about twice as accurate as VGG) on Android phones.

I don't know how many parameters Inception v3 has, but I know Google considers it more efficient than VGG ("Although our network is 42 layers deep, our computation cost is only about 2.5 higher than that of GoogLeNet and it is still much more efficient than VGGNet".)

Yes, being able to run big networks is great. But ultimately it's what you do with it, and a 3% error rate on ImageNet is a pretty compelling argument that size isn't the only factor.

[1] https://github.com/tensorflow/tensorflow/tree/master/tensorf...

[2] http://arxiv.org/abs/1512.00567

Well, it depends on the neural network you want to use. At computer vision/image processing conferences I find that it is much more common for people to use preexisting DNNs and plug them into their systems rather than train their own (why would you waste weeks or months on that?). The good pretrained DNNs available today tend to be huge, requiring a large amount of video RAM. I've talked to people writing realtime image processing software (GPU acceleration required) intended for handheld devices which can't yet actually run on the device because they don't have a suitable DNN which will fit in VRAM.

3.5% top-5 error rate on ImageNet running on Android. Hard to beat that (unless you are Microsoft Research Asia).

https://github.com/tensorflow/tensorflow/tree/master/tensorf...

mxnet has some optimised models for mobile devices - http://mxnet.readthedocs.org/en/latest/tutorial/smart_device...

Its very difficult or near impossible to do image or speech recognition realtime on mobile CPUs/GPUs. Especially with large nets

You can do limited tasks but not the kind of things we can do on desktop