There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

The organization of behavior: A neuropsychological theory

Modern computers are based on the von Neumann architecture in which computation and storage are physically separated: data are fetched from the memory unit, shuttled to the processing unit (where computation takes place) and then shuttled back to the memory unit to be stored. The rate at which data can be transferred between the processing unit and the memory unit represents a fundamental limitation of modern computers, known as the memory wall. In-memory computing is an approach that attempts to address this issue by designing systems that compute within the memory, thus eliminating the energy-intensive and time-consuming data movement that plagues current designs. Here we review the development of in-memory computing using resistive switching devices, where the two-terminal structure of the devices, their resistive switching properties, and direct data processing in the memory can enable area- and energy-efficient computation. We examine the different digital, analogue, and stochastic computing schemes that have been proposed, and explore the microscopic physical mechanisms involved. Finally, we discuss the challenges in-memory computing faces, including the required scaling characteristics, in delivering next-generation computing. This Review Article examines the development of in-memory computing using resistive switching devices.

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In-memory computing with resistive switching devices

Phase change materials are materials that exist in at least two structurally distinct solid phases, an amorphous and one (or more) crystalline phases. Many materials display phase change properties in this sense and can be deposited at least as a thin film in an amorphous phase (low temperature deposition, very thin film) or crystalline phase (high temperature deposition, epitaxy). Often the amorphous and crystalline phases have very different optical and electrical properties stemming from the large differences in structure between the amorphous and the crystalline phases. These differences can be used to store information in technological applications if it is possible to switch the material repeatedly between the two phases and if both phases are stable at operating temperature. The transformation of the metastable amorphous phase to the energetically favorable, stable crystalline phase occurs by heating the material above its crystallization temperature for a time

Phase Change Materials and Their Application to Nonvolatile Memories

With the explosive growth of digital data in the era of the Internet of Things (IoT), fast and scalable memory technologies are being researched for data storage and data-driven computation. Among the emerging memories, resistive switching memory (RRAM) raises strong interest due to its high speed, high density as a result of its simple two-terminal structure, and low cost of fabrication. The scaling projection of RRAM, however, requires a detailed understanding of switching mechanisms and there are potential reliability concerns regarding small device sizes. This work provides an overview of the current understanding of bipolar-switching RRAM operation, reliability and scaling. After reviewing the phenomenological and microscopic descriptions of the switching processes, the stability of the low- and high-resistance states will be discussed in terms of conductance fluctuations and evolution in 1D filaments containing only a few atoms. The scaling potential of RRAM will finally be addressed by reviewing the recent breakthroughs in multilevel operation and 3D architecture, making RRAM a strong competitor among future high-density memory solutions.

https://iopscience.iop.org/article/10.1088/0268-1242/31/6/063002/pdf

Resistive switching memories based on metal oxides: mechanisms, reliability and scaling

Chalcogenide materials are receiving increasing interest for their many applications as active materials in emerging memories, such as phase-change memories, programmable metallization cells, and cross-point devices. The great advantage of these materials is the capability to appear in two different phases, the amorphous and the crystalline phases, with rather different electrical properties. The aim of this work is to provide a physically based model for conduction in the amorphous chalcogenide material, able to predict the current-voltage (I−V) characteristics as a function of phase state, temperature, and cell geometry. First, the trap-limited transport at relatively low currents (subthreshold regime) is studied, leading to a comprehensive model for subthreshold conduction accounting for (a) the shape of the I−V characteristics, (b) the measured temperature dependence, (c) the dependence of subthreshold slope on the thickness of the amorphous phase, and (d) the voltage dependence of the activation ener...

Analytical model for subthreshold conduction and threshold switching in chalcogenide-based memory devices

Resistive switching memory (RRAM) devices generally rely on the formation/dissolution of conductive filaments through insulating materials, such as metal oxides and chalcogenide glasses. Understanding the mechanisms for filament formation and disruption in resistive switching materials is a critical step toward the development of reliable and controllable RRAM for future-generation storage. In particular, the capability to control the filament resistance and the reset current through the compliance current during filament formation may provide a key signature to clarify the switching mechanism. This paper provides a physically based explanation for the universal resistance switching in bipolar RRAM devices. A numerical model of filament growth based on thermally activated ion migration accounts for the resistance switching characteristics. The same physical picture is extended to numerically model the reset transition. The impact of migration parameters and experimental setup on the set/reset characteristics is discussed through numerical simulations.

Daniele Ielmini

Papers

In-memory computing with resistive switching devices

Phase Change Materials and Their Application to Nonvolatile Memories

Resistive switching memories based on metal oxides: mechanisms, reliability and scaling

Analytical model for subthreshold conduction and threshold switching in chalcogenide-based memory devices

Modeling the Universal Set/Reset Characteristics of Bipolar RRAM by Field- and Temperature-Driven Filament Growth