Energy Modeling of Machine Learning Algorithms on General Purpose Hardware

abstract: 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.Dissertation/ThesisMasters Thesis Electrical Engineering 201