Title: Using Programmable Graphene Channels as Weights in Spin-Diffusive Neuromorphic Computing,
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.