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Antiferromagnets (AFs) are remarkable magnetically ordered materials that due to the absence of a net magnetic moment do not generate dipolar fields and are insensitive to external magnetic field perturbations. However, it has been…

One of the most challenging obstacles to realizing exascale computing is minimizing the energy consumption of L2 cache, main memory, and interconnects to that memory. For promising cryogenic computing schemes utilizing Josephson junction…

Using the ultra low damping NiMnSb half-Heusler alloy patterned into vortex-state magnetic nano-dots, we demonstrate a new concept of non-volatile memory controlled by the frequency. A perpendicular bias magnetic field is used to split the…

Development of memory devices with ultimate performance has played a key role in innovation of modern electronics. As a mainstream technology nonvolatile memory devices have manifested high capacity and mechanical reliability, however…

Energy-efficient methods are addressed for leveraging low energy barrier nanomagnetic devices within neuromorphic architectures. Using a Magnetoresistive Random Access Memory (MRAM) probabilistic device (p-bit) as the basis of neuronal…

Emerging Technologies · Computer Science 2020-05-06 Hossein Pourmeidani , Punyashloka Debashis , Zhihong Chen , Ronald F. DeMara , Ramtin Zand

Future applications of spin-orbit torque will require new mechanisms to improve the efficiency for switching nanoscale magnetic tunnel junctions (MTJs), while also controlling the magnetic dynamics to achieve fast, nanosecond scale…

Mesoscale and Nanoscale Physics · Physics 2018-02-07 Shengjie Shi , Yongxi Ou , S. V. Aradhya , D. C. Ralph , R. A. Buhrman

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…

Mesoscale and Nanoscale Physics · Physics 2016-05-26 Steven Lequeux , Joao Sampaio , Vincent Cros , Kay Yakushiji , Akio Fukushima , Rie Matsumoto , Hitoshi Kubota , Shinji Yuasa , Julie Grollier

The memory wall bottleneck is a key challenge across many data-intensive applications. Multi-level FeFET-based embedded non-volatile memories are a promising solution for denser and more energy-efficient on-chip memory. However, reliable…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-06-23 Mohammad Mehdi Sharifi , Lillian Pentecost , Ramin Rajaei , Arman Kazemi , Qiuwen Lou , Gu-Yeon Wei , David Brooks , Kai Ni , X. Sharon Hu , Michael Niemier , Marco Donato

Magneto-elastic (straintronic) switching of bistable magnetostrictive nanomagnets is an extremely energy-efficient switching methodology for (magnetic) binary switches that has recently attracted widespread attention because of its…

Mesoscale and Nanoscale Physics · Physics 2019-09-18 David Winters , Md Ahsanul Abeed , Sourav Sahoo , Anjan Barman , Supriyo Bandyopadhyay

The ferroelectric material is an important platform to realize non-volatile memories. So far, existing ferroelectric memory devices utilize out-of-plane polarization in ferroelectric thin films. In this paper, we propose a new type of…

Applied Physics · Physics 2019-02-26 Huitao Shen , Junwei Liu , Kai Chang , Liang Fu

The primary impediment to continued downscaling of traditional charge-based electronic devices in accordance with Moore's law is the excessive energy dissipation that takes place in the device during switching of bits. One very promising…

Mesoscale and Nanoscale Physics · Physics 2015-04-16 Kuntal Roy

In this paper we consider unconditionally energy stable numerical schemes for the nonstationary 3D magneto-micropolar equations that describes the microstructure of rigid microelements in electrically conducting fluid flow under some…

Numerical Analysis · Mathematics 2024-03-19 Hailong Qiu

Recently, interest in programmable photonics integrated circuits has grown as a potential hardware framework for deep neural networks, quantum computing, and field programmable arrays (FPGAs). However, these circuits are constrained by the…

Magnetic skyrmions are emerging as potential candidates for next generation non-volatile memories. In this paper, we propose an in-memory binary neural network (BNN) accelerator based on the non-volatile skyrmionic memory, which we call as…

Emerging Technologies · Computer Science 2020-10-14 Venkata Pavan Kumar Miriyala , Kale Rahul Vishwanath , Xuanyao Fong

We explore how to improve the energy performance of battery-less Internet of Things (IoT) devices at the cost of a reduction in the quality of the output. Battery-less IoT devices are extremely resource-constrained energy-harvesting…

Systems and Control · Electrical Eng. & Systems 2024-03-13 Rei Barjami , Antonio Miele , Luca Mottola

Although we may be at the end of Moore's law, lowering chip power consumption is still the primary driving force for the designers. To enable low-power operation, we propose a resonant energy recovery static random access memory (SRAM). We…

Emerging Technologies · Computer Science 2020-10-06 Riadul Islam , Biprangshu Saha , Ignatius Bezzam

A coding scheme is introduced, allowing to store a set of linked bit strings in planar magnetic nanoelements with holes. Analytical expressions for the corresponding magnetization distributions are developed up to a homotopy and the…

Mesoscale and Nanoscale Physics · Physics 2023-08-02 Konstantin L. Metlov

Antiferromagnetic spintronics have attracted wide attention due to its great potential in constructing ultra-dense and ultra-fast antiferromagnetic memory that suits modern high-performance information technology. The electrical 180o…

Recently several device and circuit design techniques have been explored for applying nano-magnets and spin torque devices like spin valves and domain wall magnets in computational hardware. However, most of them have been focused on…

Disordered Systems and Neural Networks · Physics 2013-08-26 Mrigank Sharad , Charles Augustine , Kaushik Roy

Non-volatile memory, such as resistive RAM (RRAM), is an emerging energy-efficient storage, especially for low-power machine learning models on the edge. It is reported, however, that the bit error rate of RRAMs can be up to 3.3% in the…

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