Related papers: 4K-Memristor Analog-Grade Passive Crossbar Circuit
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…
Neuromorphic Computing (NC), which emulates neural activities of the human brain, is considered for low-power implementation of artificial intelligence. Towards realizing NC, fabrication, and investigations of hardware elements such as…
Solid state ionic conductors are good candidates for the next generation of nonvolatile computer memory elements. Such devices have to show reproducible resistance switching at reasonable voltage and current values even if scaled down to…
Accelerating training of artificial neural networks (ANN) with analog resistive crossbar arrays is a promising idea. While the concept has been verified on very small ANNs and toy data sets (such as MNIST), more realistically sized ANNs and…
Machine-learning tasks performed by neural networks demonstrated useful capabilities for producing reliable, and repeatable intelligent decisions. Integrated photonics, leveraging both component miniaturization and the wave-nature of the…
Monolithic three-dimensional integration of memory and logic circuits could dramatically improve performance and energy efficiency of computing systems. Some conventional and emerging memories are suitable for vertical integration,…
We have designed, fabricated, and successfully tested a prototype mixed-signal, 28x28-binary-input, 10-output, 3-layer neuromorphic network ("MLP perceptron"). It is based on embedded nonvolatile floating-gate cell arrays redesigned from a…
The memristive device is one of the basic elements of novel, brain-inspired, fast, and energy-efficient information processing systems in which there is no separation between memorization and information analysis functions. Since the first…
We demonstrate high accuracy classification for handwritten digits from the MNIST dataset ($\sim$98.00$\%$) and RGB images from the CIFAR-10 dataset ($\sim$86.80$\%$) by using resistive memories based on a 2D van-der-Waals semiconductor:…
A time-domain analog-weighted-sum calculation model based on a pulse-width modulation (PWM) approach is proposed. The proposed calculation model can be applied to any types of network structure including multi-layer feedforward networks. We…
The progress of the Internet of Things(IoT) technologies and applications requires the efficient low power circuits and architectures to maintain and improve the performance of the increasingly growing data processing systems. Memristive…
Memristors are continuously tunable resistors that emulate synapses. Conceptualized in the 1970s, they traditionally operate by voltage-induced displacements of matter, but the mechanism remains controversial. Purely electronic memristors…
Gradual switching between multiple resistance levels is desirable for analog in-memory computing using resistive random-access memory (RRAM). However, the filamentary switching of $HfO_x$-based conventional RRAM often yields only two stable…
In-memory computing (IMC) is an effectual solution for energy-efficient artificial intelligence applications. Analog IMC amortizes the power consumption of multiple sensing amplifiers with analog-to-digital converter (ADC), and…
Neuromorphic computing systems are embracing memristors to implement high density and low power synaptic storage as crossbar arrays in hardware. These systems are energy efficient in executing Spiking Neural Networks (SNNs). We observe that…
Developing electronic devices capable of emulating biological functions is essential for advancing brain-inspired computation paradigms such as neuromorphic computing. In recent years, two-dimensional materials have emerged as promising…
Brain inspired circuits can provide an alternative solution to implement computing architectures taking advantage of fault tolerance and generalisation ability of logic gates. In this brief, we advance over the memristive threshold circuit…
Memristive devices, whose resistance can be controlled by applying a voltage and further retained, are attractive as possible circuit elements for neuromorphic computing. This new type of devices poses a number of both technological and…
Dual-layer resistive switching devices with horizontal W electrodes, vertical Pd electrodes and WOx switching layer formed at the sidewall of the horizontal electrodes have been fabricated and characterized. The devices exhibit…
Nonlinearity is a crucial characteristic for implementing hardware security primitives or neuromorphic computing systems. The main feature of all memristive devices is this nonlinear behavior observed in their current-voltage…