GPUs have become increasingly adopted in the modern system as hardware accelerators in neural networks (NN), however, it introduces new security and privacy challenges. In this work, we propose a GPU hardware scheduling approach to enforce consistent power behavior throughout the NN execution that effectively incapacitate power side-channel attack from reverse engineering hyper-parameters of NN architecture.