I was reading DeepMind's paper on I2A's and realized that the sizes of the hidden layers in their model were all like 32, 64, 256, and so on: all powers of 2. I have found the same thing in other papers.
Is there any performance reason for it? Maybe related to data structure alignment?
More concretely, I would like to know if I should use this "special" sizes when training my own models.