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

TL;DR

This paper introduces a graphene-based spin-diffusive neural network that leverages tunable spin transport and nanomagnets to perform energy-efficient, scalable neuromorphic computing with programmable weights in the spin domain.

Contribution

It presents a novel graphene spintronic synapse design enabling transistor-free, energy-efficient weighted summation for neuromorphic systems, with detailed simulation validation.

Findings

Achieves energy consumption of 0.08-0.32 fJ per cell-synapse.

Demonstrates improved scalability over digital neural networks.

Validates feasibility through coupled spin/charge circuit simulations.

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.