Using Programmable Graphene Channels as Weights in Spin-Diffusive Neuromorphic Computing

Abstract

A graphene-based spin-diffusive neural network is presented in this paper 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 neuronsynapse functionality and quantify the analog weighting capability of the graphene under different spinrelaxation mechanisms. This spin-diffusive neural network using a graphene-based synapse design achieves total energy consumption of 0.55-0.97 fJ per cell·synapse and attains significantly better scalability compared to its digital counterparts, particularly as the number and bit accuracy of the synapses increases.

Keywords

• Analog weights
• graphene
• magnetic materials
• neural networks
• spin valves
• spintronics