Graphene Nanoribbon Based McCulloch-Pitts Neural Network

Abstract

In the context of an artificial intelligence and machine learning landscape that is evolving at an unprecedented pace, we propose a low power, high-speed, mixed-signal graphene nanoribbon-based (GNR) McCulloch-Pitts neuron (MCPN) implementation featuring programmable synaptic weights and inhibitory inputs. By definition, a generic MCPN is comprised of two parts, a weighted summation element and a decision element, called a soma. Our summation element implementation uses three distinct non-rectangular GNR devices, biased under specific conditions, to fulfill the roles of current source, low-side and high-side switches. The programmable excitatory and inhibitory synapses were obtained leveraging GNR SRAM cells and logic gates, hence providing the flexibility needed by real-world applications. The decision element's threshold activation function was implemented using a chain of GNR inverter structures which manifest the function's characteristic in the analog domain. Modulation of the decision element's threshold is achieved indirectly by means of a configurable resistive load which is varied depending on the configuration stored in SRAM. Our benchmark results, obtained using a generic 5 by 5 pixel pattern recognition application, reveal that the GNR-based implementation achieves 3.5× less power consumption, 20 × higher speed, while occupying 3 × less active area when compared to its FinFET analog circuit counterpart.