Related papers: A Survey of Memristive Threshold Logic Circuits
Comprehensive understanding of the world's most energy efficient powerful computer, the human brain, is an elusive scientific issue. Still, already gained knowledge indicates memristors can be used as a building block to model the brain. At…
The demand for low-dissipation nanoscale memory devices is as strong as ever. As Moore's Law is staggering, and the demand for a low-power-consuming supercomputer is high, the goal of making information processing circuits out of…
Neuromorphic computing uses brain-inspired principles to design circuits that can perform computational tasks with superior power efficiency to conventional computers. Approaches that use traditional electronic devices to create artificial…
Dynamic reconfiguration of charge carriers in confined ion-channels under electrical stimulation produces memory effects, where the internal resistance depends on history of the electric field. Vermiculite nanofluidic devices harness this…
The possible use of spin and magnets in place of charge and capacitors to store and process information is well known. Magnetic tunnel junctions are being widely investigated and developed for magnetic random access memories. These are two…
Stochastic spiking neural networks based on nanoelectronic spin devices can be a possible pathway to achieving "brainlike" compact and energy-effcient cognitive intelligence. The computational model attempt to exploit the intrinsic device…
Memristive neuromorphic systems are designed to emulate human perception and cognition, where the memristor states represent essential historical information to perform both low-level and high-level tasks. However, current systems face…
The development of novel devices for neuromorphic computing and non-traditional logic operations largely relies on the fabrication of well controlled memristive systems with functionalities beyond standard bipolar behavior and digital…
Nanoscale resistive switching devices (memristive devices or memristors) have been studied for a number of applications ranging from non-volatile memory, logic to neuromorphic systems. However a major challenge is to address the potentially…
The advent of nanoscale memristors raised hopes of being able to build CMOL (CMOS/nanowire/moLecular) type ultra-dense in-memory-computing circuit architectures. In CMOL, nanoscale memristors would be fabricated at the intersection of…
Memristive devices have shown great promise to facilitate the acceleration and improve the power efficiency of Deep Learning (DL) systems. Crossbar architectures constructed using these Resistive Random-Access Memory (RRAM) devices can be…
Spin-torque memristors were proposed in 2009, which could provide fast, low-power and infinite memristive behavior for large-density non-volatile memory and neuromorphic computing. However, the strict requirements of combining high…
To obtain precisely controllable, robust as well as reproduceable memristor for efficient neuromorphic computing still very challenging. Molecular tailoring aims at obtaining the much more flexibly tuning plasticity has recently generated…
Memristors provide a tempting solution for weighted synapse connections in neuromorphic computing due to their size and non-volatile nature. However, memristors are unreliable in the commonly used voltage-pulse-based programming approaches…
This paper gives an overview of recent progress in the brain inspired computing field with a focus on implementation using emerging memories as electronic synapses. Design considerations and challenges such as requirements and design…
Memristive nanodevices offer new frontiers for computing systems that unite arithmetic and memory operations on-chip. Here, we explore the integration of electrochemical metallization cell (ECM) nanodevices with tunable filamentary…
It is now widely accepted that memristive devices are perfect candidates for the emulation of biological synapses in neuromorphic systems. This is mainly because of the fact that like the strength of synapse, memristance of the memristive…
The development of memristive device technologies has reached a level of maturity to enable the design of complex and large-scale hybrid memristive-CMOS neural processing systems. These systems offer promising solutions for implementing…
Memristors are low-power memory-holding resistors thought to be useful for neuromophic computing, which can compute via spike-interactions mediated through the device's short-term memory. Using interacting spikes, it is possible to build an…
In pursuit of neuromorphic (brain-inspired) devices, memristors (memory-resistors) have emerged as effective components for emulating neuronal circuitry. Here we formally define a class of Simple Volatile Memristors (SVMs) based on a simple…