volume that is n and the reciprocal of the squared dimensions of the kernel DK to put this into perspective of how effective depth wise convolution is let us take an example so consider the output feature volume n of 1024 and a kernel of size 3 that's DK is equal to 3 plugging these values into the relation we get zero point 1 1 2 in other words standard convolution has 9 times more the number of multiplications as that of depth Y separable convolution this is a lot of computing power we can also quickly compare the number of parameters