Re:Emergent Chip Vastly Accelerates Deep Neural Networks
by nl on Wednesday January 13, 2016 @12:16AM
(#10892442)
[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.
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