In this paper, we propose an efficient predefined structured sparsity-based ex-situ training framework for a hybrid CMOS-memristive neuromorphic hardware for deep neural network to significantly lower the power consumption and computational complexity and improve scalability. The structure is verified on a wide range of datasets including MNIST handwritten recognition, breast cancer prediction, and mobile health monitoring. The results of this study show that compared to its fully connected version, the proposed structure provides significant power reduction while maintaining high classification accuracy.
@article{arxiv.1809.03476,
title = {SpRRAM: A Predefined Sparsity Based Memristive Neuromorphic Circuit for Low Power Application},
author = {Arash Fayyazi and Souvik Kundu and Shahin Nazarian and Peter A. Beerel and Massoud Pedram},
journal= {arXiv preprint arXiv:1809.03476},
year = {2018}
}