Electronic system with memristive synapses for pattern recognition

Abstract

Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system. The system learns, and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i /, and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction.

Face classification using electronic synapses

“…Besides, they are compatible with CMOS fabrication process303132 and can be scaled down33 remarkably to reach density as high as 10 11 synapses per cm 2 . Although continuous conductance modulation behaviour on a single resistive switching device and simple neuromorphic computing on a small resistive array were reported recently1430, to our knowledge, large neuromorphic network utilizing the bidirectional analogue behaviour of resistive switching synapse for face classification task is not realized yet. This is due to the nature of imperfection of the device111315, such as abrupt switching during SET, the variation between each cell and the fluctuation during repeated cycles.…”

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

“…The former relies on memristors featuring only two states, high resistance state (HRS) or low resistance state (LRS), and it is proved to be effective in specific applications (Suri et al, 2013; Wang et al, 2015; Ambrogio et al, 2016a). On the other hand, analog evolution of device resistance is desirable to improve the robustness of the network (Bill and Legenstein, 2014; Garbin et al, 2015; Park et al, 2015), but the difficulty of operating memristors in an analog fashion renders hardware implementations of networks with analog synapses still challenging (Garbin et al, 2015). Indeed, several memristors show only a partial analog behavior, either when increasing the resistance (synaptic depression), which is common in filamentary devices as oxide-based memristors (Kuzum et al, 2013; Yu et al, 2013a), or when decreasing the resistance (synaptic potentiation) as in some kinds of phase change memristors (Eryilmaz et al, 2014).…”

Analogue signal and image processing with large memristor crossbars

“…So far, demonstrations of this concept have been limited to binary signal input and/or binary matrix weights [14][15][16] . Recently, pulse width, instead of amplitude, was used to represent the analogue input signals [27][28][29][30] , but this scheme requires more readout time and more complicated integrated circuits.…”