A graphene-based spin-diffusive (GrSD) neural network is presented in this work that takes advantage of the locally tunable spin transport of graphene and the non-volatility of nanomagnets. By using electrostatically gated graphene as spintronic synapses, a weighted summation operation can be performed in the spin domain while the weights can be programmed using circuits in the charge domain. Four-component spin/charge circuit simulations coupled to magnetic dynamics are used to show the feasibility of the neuron-synapse functionality and quantify the analog weighting capability of the graphene under different spin relaxation mechanisms. By realizing transistor-free weight implementation, the graphene spin-diffusive neural network reduces the energy consumption to 0.08-0.32 fJ per cell-synapse and achieves significantly better scalability compared to its digital counterparts, particularly as the number and bit accuracy of the synapses increases.
@article{arxiv.1712.00550,
title = {Using Programmable Graphene Channels as Weights in Spin-Diffusive Neuromorphic Computing},
author = {Jiaxi Hu and Gordon Stecklein and Yoska Anugrah and Paul A. Crowell and Steven J. Koester},
journal= {arXiv preprint arXiv:1712.00550},
year = {2017}
}