Stochasticity in an Artificial Neuron using Ag/2D-MoS2/Au Threshold Switching Memristor

Neuromorphic computing comprises of systems that are based on the human brain or artificial neural networks, with the promise of creating a brain inspired ability to learn and adapt, but technical challenges, such as developing an accurate neuroscience model of the functionality of the brain to building devices to support these models, are significantly hindering the progress of neuromorphic systems. This has paved the way for artificial neural networks (ANN) to meet these criteria. The memristor has become an emerging candidate to realize ANN through emulation synapse and neuron behavior. In this work, we are fabricating an Ag/MoS­2/Au threshold switching memristor (TSM), to emulate four critical behaviors of neurons - all-or-nothing spiking, threshold-driven firing, post firing refractory period and stimulus strength-based frequency response. We will also test the innate stochastic behavior of these devices to see if they are voltage dependent, making them a possible application in the integrate and fire neuron. Continuing to emulate biological synapses using memristors can help solve many optimization and machine learning problems, which in turn, can make electronics as energy-efficient as our brain

Review on data-centric brain-inspired computing paradigms exploiting emerging memory devices

Biologically-inspired neuromorphic computing paradigms are computational platforms that imitate synaptic and neuronal activities in the human brain to process big data flows in an efficient and cognitive manner. In the past decades, neuromorphic computing has been widely investigated in various application fields such as language translation, image recognition, modeling of phase, and speech recognition, especially in neural networks (NNs) by utilizing emerging nanotechnologies; due to their inherent miniaturization with low power cost, they can alleviate the technical barriers of neuromorphic computing by exploiting traditional silicon technology in practical applications. In this work, we review recent advances in the development of brain-inspired computing (BIC) systems with respect to the perspective of a system designer, from the device technology level and circuit level up to the architecture and system levels. In particular, we sort out the NN architecture determined by the data structures centered on big data flows in application scenarios. Finally, the interactions between the system level with the architecture level and circuit/device level are discussed. Consequently, this review can serve the future development and opportunities of the BIC system design