How does inception decrease the computational cost? From the second paragraph of 3.1 Factorization into smaller convolution in the paper Rethinking the inception architecture for computer vision:


  This setup clearly reduces the parameter count by shar- ing the weights between adjacent tiles. To analyze the expected computational cost savings,
  
  ...
  
  This way, we end up with a net 9+9 × reduction of computation, resulting
  25 in a relative gain of 28% by this factorization


Apprantly this design decreases the number of parameters. But I can't understand why it decreases the computational cost? 

For the case of using 2 3*3 convnets to replace 5*5, I think it increases (3*3*9+3*3)/5*5 = 3.6 times of computational cost. 

What do I miss here?
However, in the overall scheme, sliding this network can be represented by two 3 x 3 convolutional layers which reuses the activation between adjacent tiles. 


Since the replacement (two 3x3 instead of one 5x5) share weights, we don't have to calculate them twice. That is where the gain comes from.  

Edit: 

The gain comes from the sliding: using the following formula (n-filtersize+1) x (n-filtersize+1) that calculates the output of a filter on an n x n input 

a more detailed answer can be found here: 
Reducing Filter Size in Convolutional Neural Network 
Thank you @Thomas W.