Related papers: 4K-Memristor Analog-Grade Passive Crossbar Circuit
The memristor is promising to be the basic cell of next-generation computation systems. Compared to the traditional MOSFET device, the memristor is efficient over energy and area. But one of the biggest challenges faced with researchers is…
Resistance switching devices are of special importance because of their application in resistive memories (RRAM) which are promising candidates for replacing current nonvolatile memories and realize storage class memories. These devices…
This article proposes a general approach to the simulation and design of a multilayer perceptron (MLP) network on the basis of cross-bar arrays of metal-oxide memristive devices. The proposed approach uses the ANNM theory, tolerance theory,…
In the last decade, a 2-terminal passive circuit element called a memristor has been developed for non-volatile resistive random access memory and has more recently shown promise for neuromorphic computing. Compared to flash memory,…
In this paper, we propose an efficient predefined structured sparsity-based ex-situ training framework for a hybrid CMOS-memristive neuromorphic hardware for deep neural network to significantly lower the power consumption and computational…
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…
Memristors are promising devices for scalable and low power, in-memory computing to improve the energy efficiency of a rising computational demand. The crossbar array architecture with memristors is used for vector matrix multiplication…
In-memory computing (IMC) enables energy-efficient neural network inference by computing analog matrix-vector multiplications (MVM) in memory crossbar arrays. In this work we present a simulation framework for N-ary crossbar architectures…
A Ferroelectric Analog Non-Volatile Memory based on a WOx electrode and ferroelectric HfZrO$_4$ layer is fabricated at a low thermal budget (~375$^\circ$C), enabling BEOL processes and CMOS integration. The devices show suitable properties…
The need to repeatedly shuttle around synaptic weight values from memory to processing units has been a key source of energy inefficiency associated with hardware implementation of artificial neural networks. Analog in-memory computing…
Amorphous insulators have localized wave functions that decay with the distance $r$ following exp($-r/\zeta$). Since nanoscale conduction is not excluded at $r<\zeta$, one may use amorphous insulators and take advantage of their size effect…
This paper proposes four quadrant analog multiplier using CMOS-memristor circuit. Currently, there are plenty of analog multipliers using resistors and CMOS transistors. They can attain perfect multiplication but have several disadvantages…
Memristors are non-volatile nano-resistors. Their resistance can be tuned by applied currents or voltages and set to a large number of levels between two limit values. Thanks to these properties, memristors are ideal building blocks for a…
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…
Memristors are nonlinear two-terminal circuit elements whose resistance at a given time depends on past electrical stimuli. Recently, networks of memristors have received attention in neuromorphic computing since they can be used as a tool…
Neuromorphic devices, with their distinct advantages in energy efficiency and parallel processing, are pivotal in advancing artificial intelligence applications. Among these devices, memristive transistors have attracted significant…
On-device intelligence is gaining significant attention recently as it offers local data processing and low power consumption. In this research, an on-device training circuitry for threshold-current memristors integrated in a crossbar…
On-chip learning in a crossbar array based analog hardware Neural Network (NN) has been shown to have major advantages in terms of speed and energy compared to training NN on a traditional computer. However analog hardware NN proposals and…
Memristors can mimic the functions of biological synapse, where it can simultaneously store the synaptic weight and modulate the transmitted signal. Here, we report Nb/Nb2O5/Pt based memristors with bipolar resistive switching, exhibiting…
Crossbar architectures have long been seen as a promising foundation for in-memory computing, using memristor arrays for high-density, energy-efficient analog computation. However, this conventional architecture suffers from a fundamental…