Three-Dimensional Neuromorphic Computing System With Two-Layer and Low-Variation Memristive Synapses

Abstract

Three-dimensional Integrated Circuits (3D-ICs) is a cutting-edge design methodology of placing the circuitry vertically aiming for a high-speed and energy-efficient system with the smallest design area. In this paper, a novel 3D neuromorphic system is proposed and analyzed, which utilizes the fabricated two-layer memristor as the electronic synapses in a spiking neural network (SNN). The two-layer structure of the memristors leads to a significant improvement in the design area (2×), power consumption (1.48 ×), and latency (2.58×), compared to the traditional one-layer configuration. Meanwhile, the heat dissipation layers are added to our memristors reducing 30% cycle-to-cycle switching variation. Our memristive synapses are utilized for storing the exported weights of the SNNs that have threshold function as the activation function. The proposed neuromorphic system is evaluated using a hardware-software co-design approach importing the weights of SNNs into NeuroSIM. The simulation results demonstrate the significant improvement of memristive synapses on design area, power consumption, and latency, compared with the SRAM (Static Random-access Memory) and other state-of-the-art memristive synapses (10% to 66%).