Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition

A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density. Previous hybrid analog CMOS-memristor approaches required extensive CMOS circuitry for training, and thus eliminated most of the density advantages gained by the adoption of memristor synapses. Further, they used different waveforms for pre and post-synaptic spikes that added undesirable circuit overhead. Here we describe a hardware architecture that can feature a large number of memristor synapses to learn real-world patterns. We present a versatile CMOS neuron that combines integrate-and-fire behavior, drives passive memristors and implements competitive learning in a compact circuit module, and enables in-situ plasticity in the memristor synapses. We demonstrate handwritten-digits recognition using the proposed architecture using transistor-level circuit simulations. As the described neuromorphic architecture is homogeneous, it realizes a fundamental building block for large-scale energy-efficient brain-inspired silicon chips that could lead to next-generation cognitive computing.Comment: This is a preprint of an article accepted for publication in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol 5, no. 2, June 201

Memristive Systems Based on Two-Dimensional Materials

The unique electronic and optical properties of newly discovered 2D crystals such as graphene, graphene oxide, molybdenum disulfide, and so on demonstrate the tremendous potential in creating ultrahigh-density nano- and bioelectronics for innovative image recognition systems, storage and processing of big data. A new type of memristors with a floating photogate based on biocompatible graphene and other 2D crystals with extremely low power consumption and footprint is considered. The photocatalytic oxidation of graphene is proposed as an effective method of creating synapse-like 2D memristive devices with photoresistive switching for nonvolatile electronic memory of ultrahigh density. Particular attention is paid to the new concept of the formation of self-assembled nanoscale memristive elements interfacing artificial electronic neural networks. 2D photomemristors with a floating photogate exhibit multiple states controlled in a wide range of electromagnetic radiation and can be used for neuromorphic computations, pattern recognition and image processing needed to create artificial intelligence