Graphene-based neuromorphic computing

Abstract

The human brain is a natural high-performance computing systemwith outstanding properties, e.g., ultra-low energy consumption, highly parallel information processing, suitability for solving complex tasks, and robustness. As such, numerous attempts have been made to devise neuromorphic systems able to achieve brain-akin computation abilities, which can aid in understanding the complex human brain functionality and can be utilized to solve complex problems, e.g., pattern recognition and data mining. However, the fact that human brain comprises billions of neurons, which are the fundamental information processing units, and trillions of synapses that interconnect them makes the design and implementation of large-scale brain-inspired computing systems quite a challenging task. Graphene appears to be a promising candidate for scalable neuromorphic implementations as it exhibits a wealth of outstanding properties, e.g., ballistic transport, ultimate thinness, flexibility, and graphene devices are capable of emulating complex nonlinear functions and can be readily tuned to provide various conduction dynamicswhile preserving low energy operation and small footprint. Moreover, graphene is biocompatible, which offers perspectives for graphene-based neuromorphic bio-interfaces. This thesis aims to investigate graphene’s potential to enable scalable and energy effective neuromorphic computing. To this end, we first introduce an atomistic-level simulation model for calculating graphene electronic transport properties, that captures the hysteresis effects induced by interface charges trapping/detrapping phenomena. Second, we propose a generic graphene based synapse, which can be tailored to emulate different synaptic plasticity types by properly modifying its Graphene NanoRibbon (GNR) shape and contacts topology, as well as applying external voltages. Subsequently, we introduce a compact graphene-based integrate-and-fire spiking neuron that mimics the basic spiking neuronal dynamics. We further propose a basic SpikingNeuralNetwork (SNN) unit,which can be utilized to implement complex graphene-based SNNstructures. Finally,we introduce a reconfigurable graphene-based SNN architecture and a training methodology for obtaining the initial SNN synaptic weight values. We demonstrate the feasibility of the synaptic weights training methodology and the practical capabilities of the proposedSNNarchitecture by applying them to solve character recognition and edge detection problems. Our experiments clearly indicate that the proposed graphene-based neuromorphic approach enables lowenergy operation at small chip real estate footprint, which are enabling factors for the realization of scalable energy-efficient SNN implementations.