In this chapter, we will restrict our attention to the ferrites and a few other closely related materials. The great interest in ferrites stems from their unique combination of a spontaneous magnetization and a high electrical resistivity. The observed magnetization results from the difference in the magnetizations of two non-equivalent sub-lattices of the magnetic ions in the crystal structure. Materials of this type should strictly be designated as “ferrimagnetic” and in some respects are more closely related to anti-ferromagnetic substances than they are to ferromagnetics in which the magnetization results from the parallel alignment of all the magnetic moments present. We shall not adhere to this special nomenclature except to emphasize effects, which are due to the existence of the sub-lattices.

Ferromagnetic materials

Traditional von Neumann computing systems involve separate processing and memory units. However, data movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the traditional systems and one such non-von Neumann computational approach is in-memory computing. Hereby certain computational tasks are performed in place in the memory itself by exploiting the physical attributes of the memory devices. Both charge-based and resistance-based memory devices are being explored for in-memory computing. In this Review, we provide a broad overview of the key computational primitives enabled by these memory devices as well as their applications spanning scientific computing, signal processing, optimization, machine learning, deep learning and stochastic computing. This Review provides an overview of memory devices and the key computational primitives for in-memory computing, and examines the possibilities of applying this computing approach to a wide range of applications.

Memory devices and applications for in-memory computing

Neuromorphic computers comprised of artificial neurons and synapses could provide a more efficient approach to implementing neural network algorithms than traditional hardware. Recently, artificial neurons based on memristors have been developed, but with limited bio-realistic dynamics and no direct interaction with the artificial synapses in an integrated network. Here we show that a diffusive memristor based on silver nanoparticles in a dielectric film can be used to create an artificial neuron with stochastic leaky integrate-and-fire dynamics and tunable integration time, which is determined by silver migration alone or its interaction with circuit capacitance. We integrate these neurons with nonvolatile memristive synapses to build fully memristive artificial neural networks. With these integrated networks, we experimentally demonstrate unsupervised synaptic weight updating and pattern classification.

/pdf/fully-memristive-neural-networks-for-pattern-classification-dtv92p7uso.pdf

Fully memristive neural networks for pattern classification with unsupervised learning

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

The rapid increase in information in the big-data era calls for changes to information-processing paradigms, which, in turn, demand new circuit-building blocks to overcome the decreasing cost-effectiveness of transistor scaling and the intrinsic inefficiency of using transistors in non-von Neumann computing architectures. Accordingly, resistive switching materials (RSMs) based on different physical principles have emerged for memories that could enable energy-efficient and area-efficient in-memory computing. In this Review, we survey the four physical mechanisms that lead to such resistive switching: redox reactions, phase transitions, spin-polarized tunnelling and ferroelectric polarization. We discuss how these mechanisms equip RSMs with desirable properties for representation capability, switching speed and energy, reliability and device density. These properties are the key enablers of processing-in-memory platforms, with applications ranging from neuromorphic computing and general-purpose memcomputing to cybersecurity. Finally, we examine the device requirements for such systems based on RSMs and provide suggestions to address challenges in materials engineering, device optimization, system integration and algorithm design. Resistive switching materials enable novel, in-memory information processing, which may resolve the von Neumann bottleneck. This Review focuses on how the switching mechanisms and the resultant electrical properties lead to various computing applications.

Resistive switching materials for information processing

Memristors are passive electrical components that can act like simple memories. Here, the authors use an array of hafnium oxide memristors to create a type of artificial neural network, known as a Hopfield network, that is capable of retrieving data from partial information

/pdf/associative-memory-realized-by-a-reconfigurable-memristive-25s3lz8d7o.pdf

Associative memory realized by a reconfigurable memristive Hopfield neural network

We study the paired-pulse-induced response of a NiOx-based memristor. The behavior of the memristor is surprisingly similar to the paired-pulse facilitation of a biological synapse. When the memristor is stimulated with a pair of electrical pulses, the current of the memristor induced by the second pulse is larger than that by the first pulse. In addition, the magnitude of the facilitation decreases with the pulse interval, while it increases with the pulse magnitude or pulse width.

/pdf/emulating-the-paired-pulse-facilitation-of-a-biological-1zmf4vfz65.pdf

Emulating the paired-pulse facilitation of a biological synapse with a NiOx-based memristor

The well-known Ebbinghaus forgetting curve, which describes how information is forgotten over time, can be emulated using a NiO-based memristor with conductance that decreases with time after the application of electrical pulses. Here, the conductance is analogous to the memory state, while each electrical pulse represents a memory stimulation or learning event. The decrease in the conductance with time depends on the stimulation parameters, including pulse height and width and the number of pulses, which emulates memory loss behavior well in that the time taken for the memory to be lost depends on how the information is learned.

/pdf/emulating-the-ebbinghaus-forgetting-curve-of-the-human-brain-1li2c8hqyy.pdf

Emulating the Ebbinghaus forgetting curve of the human brain with a NiO-based memristor

Resistive switching memristor, predicted as the fourth fundamental passive circuit element (in addition to resistor, capacitor, and inductor) in the world by Leon Chua in 1971 and demonstrated by Hewlett Packard laboratory in 2008, has attracted extensive research attention for its high scalability and versatility. In this paper, we reviewed the working mechanisms and mathematical models of nanostructured resistive switching memristors, and examined various emerging applications of the memristors, including memory, analog, logic and neuromorphic applications.

Review of Nanostructured Resistive Switching Memristor and Its Applications

Synaptic Long-Term Potentiation (LTP), which is a long-lasting enhancement in signal transmission between neurons, is widely considered as the major cellular mechanism during learning and memorization. In this work, a NiOx-based memristor is found to be able to emulate the synaptic LTP. Electrical conductance of the memristor is increased by electrical pulse stimulation and then spontaneously decays towards its initial state, which resembles the synaptic LTP. The lasting time of the LTP in the memristor can be estimated with the relaxation equation, which well describes the conductance decay behavior. The LTP effect of the memristor has a dependence on the stimulation parameters, including pulse height, width, interval, and number of pulses. An artificial network consisting of three neurons and two synapses is constructed to demonstrate the associative learning and LTP behavior in extinction of association in Pavlov's dog experiment.

/pdf/synaptic-long-term-potentiation-realized-in-pavlov-s-dog-2ymt2sulxi.pdf

L. J. Deng

Papers

Associative memory realized by a reconfigurable memristive Hopfield neural network

Emulating the paired-pulse facilitation of a biological synapse with a NiOx-based memristor

Emulating the Ebbinghaus forgetting curve of the human brain with a NiO-based memristor

Review of Nanostructured Resistive Switching Memristor and Its Applications

Synaptic long-term potentiation realized in Pavlov's dog model based on a NiOx-based memristor