Articial Neural Network(ANN) has become a for-bearer in the field of Articial Intel-

ligence. The innovations in ANN has led to ground breaking technological advances

like self-driving vehicles,medical diagnosis,speech Processing,personal assistants and

many more. These were inspired by evolution and working of our brains. Similar

to how our brain evolved using a combination of epigenetics and live stimulus,ANN

require training to learn patterns.The training usually requires a lot of computation

and memory accesses. To realize these systems in real embedded hardware many

Energy/Power/Performance issues needs to be solved. The purpose of this research

is to focus on methods to study data movement requirement for generic Neural Net-

work along with the energy associated with it and suggest some ways to improve the

design.Many methods have suggested ways to optimize using mix of computation and

data movement solutions without affecting task accuracy. But these methods lack a

computation model to calculate the energy and depend on mere back of the envelope calculation. We realized that there is a need for a generic quantitative analysis

for memory access energy which helps in better architectural exploration. We show

that the present architectural tools are either incompatible or too slow and we need

a better analytical method to estimate data movement energy. We also propose a

simplistic yet effective approach that is robust and expandable by users to support

various systems.