Applications of Emerging Memory Technology

Compositional Tuning of Negative Differential Resistance in Bulk Silver Iodide Memristor

New Journal of Chemistry ◽

10.1039/d0nj05427e ◽

2020 ◽

Author(s):

SMITA GAJANAN NAIK ◽

Mohammad Hussain Kasim Rabinal

Keyword(s):

Power Consumption ◽

Negative Differential Resistance ◽

Silver Iodide ◽

Differential Resistance ◽

Fast Response ◽

Low Power Consumption ◽

Switching Effect ◽

Memory Switching ◽

Bulk Silver ◽

Emerging Memory

Electrical memory switching effect has received a great interest to develop emerging memory technology such as memristors. The high density, fast response, multi-bit storage and low power consumption are their...

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Towards Neuromorphic Learning Machines Using Emerging Memory Devices with Brain-Like Energy Efficiency

Journal of Low Power Electronics and Applications ◽

10.3390/jlpea8040034 ◽

2018 ◽

Vol 8 (4) ◽

pp. 34 ◽

Cited By ~ 10

Author(s):

Vishal Saxena ◽

Xinyu Wu ◽

Ira Srivastava ◽

Kehan Zhu

Keyword(s):

Machine Learning ◽

Energy Efficiency ◽

Deep Learning ◽

Large Scale ◽

Learning Algorithms ◽

Memory Devices ◽

Cognitive Computing ◽

Mixed Signal ◽

Von Neumann ◽

Emerging Memory

The ongoing revolution in Deep Learning is redefining the nature of computing that is driven by the increasing amount of pattern classification and cognitive tasks. Specialized digital hardware for deep learning still holds its predominance due to the flexibility offered by the software implementation and maturity of algorithms. However, it is being increasingly desired that cognitive computing occurs at the edge, i.e., on hand-held devices that are energy constrained, which is energy prohibitive when employing digital von Neumann architectures. Recent explorations in digital neuromorphic hardware have shown promise, but offer low neurosynaptic density needed for scaling to applications such as intelligent cognitive assistants (ICA). Large-scale integration of nanoscale emerging memory devices with Complementary Metal Oxide Semiconductor (CMOS) mixed-signal integrated circuits can herald a new generation of Neuromorphic computers that will transcend the von Neumann bottleneck for cognitive computing tasks. Such hybrid Neuromorphic System-on-a-chip (NeuSoC) architectures promise machine learning capability at chip-scale form factor, and several orders of magnitude improvement in energy efficiency. Practical demonstration of such architectures has been limited as performance of emerging memory devices falls short of the expected behavior from the idealized memristor-based analog synapses, or weights, and novel machine learning algorithms are needed to take advantage of the device behavior. In this article, we review the challenges involved and present a pathway to realize large-scale mixed-signal NeuSoCs, from device arrays and circuits to spike-based deep learning algorithms with ‘brain-like’ energy-efficiency.

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A review of interconnect materials used in emerging memory device packaging: first- and second-level interconnect materials

Journal of Materials Science Materials in Electronics ◽

10.1007/s10854-021-07105-9 ◽

2021 ◽

Author(s):

Yong Sheng Zou ◽

Chong Leong Gan ◽

Min-Hua Chung ◽

Hem Takiar

Keyword(s):

Memory Device ◽

Emerging Memory ◽

Materials Used

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BIST Architecture for Magnetic Memories

Mathematical Problems of Computer Science ◽

10.51408/1963-0062 ◽

2020 ◽

pp. 88-95

Author(s):

Armen Babayan

Keyword(s):

Random Access ◽

Random Access Memory ◽

Test Solution ◽

Access Memory ◽

Magnetic Random Access Memory ◽

Emerging Memory ◽

Self Test ◽

Built In Self Test

Magnetic random-access memory (MRAM) is one of the emerging memory technologies, which can be considered as the next universal memory because of its good parameters. Nevertheless, this type of memory is not guaranteed from defects and it is very important to understand the fault typology and develop a test solution that addresses these faults. In this paper a Built-in Self-Test (BIST) solution is presented, which is specifically tailored for MRAMs and efficiently deals with MRAM specific faults.

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Editorial for the Special Issue on Emerging Memory and Computing Devices in the Era of Intelligent Machines

Micromachines ◽

10.3390/mi11010073 ◽

2020 ◽

Vol 11 (1) ◽

pp. 73

Author(s):

Pedram Khalili Amiri

Keyword(s):

Energy Efficiency ◽

System Level ◽

Special Issue ◽

Computing Systems ◽

Intelligent Machines ◽

Emerging Memory ◽

Overall Performance

Computing systems are undergoing a transformation from logic-centric toward memory-centric architectures, where overall performance and energy efficiency at the system level are determined by the density, bandwidth, latency, and energy efficiency of the memory, rather than the logic sub-system [...]

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