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Crossbar resistive memory with 1 Selector 1 Resistor (1S1R) structure is attractive for low-cost and high-density nonvolatile memory applications. As technology scales down to the single-nm regime, the increasing resistivity of…

Systems and Control · Electrical Eng. & Systems 2020-10-14 Zehui Chen , Lara Dolecek

Crossbar arrays using emerging non-volatile memory technologies such as Resistive RAM (ReRAM) offer high density, fast access speed and low-power. However the bandwidth of the crossbar is limited to single-bit read/write per access to avoid…

Emerging Technologies · Computer Science 2016-06-03 Mohammad Nasim Imtiaz Khan , Swaroop Ghosh , Radha Krishna Aluru , Rashmi Jha

Passive crossbar arrays based upon memristive devices, at crosspoints, hold great promise for the future high-density and non-volatile memories. The most significant challenge facing memristive device based crossbars today is the problem of…

Emerging Technologies · Computer Science 2015-07-09 Yansong Gao , Omid Kavehei , Damith C. Ranasinghe , Said F. Al-Sarawi , Derek Abbott

Due to the crossbar array architecture, the sneak-path problem severely degrades the data integrity in the resistive random access memory (ReRAM). In this letter, we investigate the channel quantizer design for ReRAM arrays with multiple…

Information Theory · Computer Science 2024-10-08 Zhen Mei , Kui Cai , Long Shi , Jun Li

Crossbar resistive memory with the 1 Selector 1 Resistor (1S1R) structure is attractive for nonvolatile, high-density, and low-latency storage-class memory applications. As technology scales down to the single-nm regime, the increasing…

Systems and Control · Electrical Eng. & Systems 2021-04-30 Zehui Chen , Lara Dolecek

Resistive Random Access Memory (RRAM) crossbar arrays are an attractive memory structure for emerging nonvolatile memory due to their high density and excellent scalability. Their ability to perform logic operations using RRAM devices makes…

Hardware Architecture · Computer Science 2024-07-16 Arjun Tyagi , Shahar Kvatinsky

Resistive memories store information in a crossbar arrangement of two-terminal devices that can be programmed to patterns of high or low resistance. While extremely compact, this technology suffers from the "sneak-path" problem: certain…

Information Theory · Computer Science 2022-02-15 Susanna E. Rumsey , Stark C. Draper , Frank R. Kschischang

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…

Optimization and Control · Mathematics 2024-09-24 Marieke Heidema , Henk van Waarde , Bart Besselink

Memristor devices are crucial for developing neuromorphic computers and next-generation memory technologies. In this work, we provide a comprehensive modelling tool for simulating static DC reading operations of memristor crossbar arrays…

Neural and Evolutionary Computing · Computer Science 2019-10-23 Yasir J Noori , C H de Groot

Redox-based resistive switching devices (ReRAM) are an emerging class of non-volatile storage elements suited for nanoscale memory applications. In terms of logic operations, ReRAM devices were suggested to be used as programmable…

Emerging Technologies · Computer Science 2015-03-02 A. Siemon , S. Menzel , R. Waser , E. Linn

As conventional memory technologies are challenged by their technological physical limits, emerging technologies driven by novel materials are becoming an attractive option for future memory architectures. Among these technologies,…

Materials Science · Physics 2015-05-28 Omid Kavehei , Said Al-Sarawi , Kyoung-Rok Cho , Kamran Eshraghian , Derek Abbott

Developing ultra-low-energy superconducting computing and fault-tolerant quantum computing will require scalable superconducting memory. While conventional superconducting logic-based memory cells have facilitated early demonstrations,…

A resistive memory network that has no crossover wiring is proposed to overcome the hardware limitations to size and functional complexity that is associated with conventional analogue neural networks. The proposed memory network is based…

Artificial Intelligence · Computer Science 2012-01-31 Alex Pappachen James , Sima Dimitrijev

Deoxyribonucleic acid (DNA) has emerged as a promising building block for next-generation ultra-high density storage devices. Although DNA has high durability and extremely high density in nature, its potential as the basis of storage…

Emerging Technologies · Computer Science 2023-04-28 Arpan De , Hashem Mohammad , Yiren Wang , Rajkumar Kubendran , Arindam K. Das , M. P. Anantram

In-memory computing is a promising alternative to traditional computer designs, as it helps overcome performance limits caused by the separation of memory and processing units. However, many current approaches struggle with unreliable…

Machine learning has been getting a large attention in the recent years, as a tool to process big data generated by ubiquitous sensors in our daily life. High speed, low energy computing machines are in demand to enable real-time artificial…

Machine Learning · Computer Science 2020-05-06 Zhong Sun , Giacomo Pedretti , Alessandro Bricalli , Daniele Ielmini

This paper describes a new memristor crossbar architecture that is proposed for use in a high density cache design. This design has less than 10% of the write energy consumption than a simple memristor crossbar. Also, it has up to 4 times…

Hardware Architecture · Computer Science 2013-04-10 Chris Yakopcic , Tarek M. Taha

Memristor crossbar arrays are used in a wide range of in-memory and neuromorphic computing applications. However, memristor devices suffer from non-idealities that result in the variability of conductive states, making programming them to a…

Emerging Technologies · Computer Science 2021-05-13 A. P. James , L. O. Chua

This brief introduces a read bias circuit to improve readout yield of magnetic random access memories (MRAMs). A dynamic bias optimization (DBO) circuit is proposed to enable the real-time tracking of the optimal read voltage across…

Signal Processing · Electrical Eng. & Systems 2023-09-19 Renhe Chen , Albert Lee , Zirui Wang , Di Wu , Xufeng Kou

Artificial Neural Network computation relies on intensive vector-matrix multiplications. Recently, the emerging nonvolatile memory (NVM) crossbar array showed a feasibility of implementing such operations with high energy efficiency, thus…

Emerging Technologies · Computer Science 2017-04-03 Hyungjun Kim , Taesu Kim , Jinseok Kim , Jae-Joon Kim
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