1. Using Programmable Graphene Channels as Weights in Spin-Diffusive Neuromorphic Computing
- Author
-
Hu, Jiaxi, Stecklein, Gordon, Anugrah, Yoska, Crowell, Paul A., and Koester, Steven J.
- Subjects
Physics - Applied Physics ,Condensed Matter - Disordered Systems and Neural Networks ,Condensed Matter - Mesoscale and Nanoscale Physics - Abstract
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., Comment: 8 pages, 12 figures
- Published
- 2017