scholarly journals Emerging Memory Devices for Neuromorphic Computing

2019 ◽  
Vol 4 (4) ◽  
pp. 1800589 ◽  
Author(s):  
Navnidhi K. Upadhyay ◽  
Hao Jiang ◽  
Zhongrui Wang ◽  
Shiva Asapu ◽  
Qiangfei Xia ◽  
...  
Keyword(s):  
Memory Devices ◽  
Neuromorphic Computing ◽  
Emerging Memory

scholarly journals Neuromorphic Computing Using Emerging Synaptic Devices: A Retrospective Summary and an Outlook

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1414
Author(s):  
Jaeyoung Park
Keyword(s):  
Human Brain ◽  
Memory Devices ◽  
Approximate Computing ◽  
Promising Candidate ◽  
Neuromorphic Computing ◽  
Fast Speed ◽  
Emerging Memory ◽  
Inherent Error ◽  
Atomic Switch ◽  
Computing Approach

In this paper, emerging memory devices are investigated for a promising synaptic device of neuromorphic computing. Because the neuromorphic computing hardware requires high memory density, fast speed, and low power as well as a unique characteristic that simulates the function of learning by imitating the process of the human brain, memristor devices are considered as a promising candidate because of their desirable characteristic. Among them, Phase-change RAM (PRAM) Resistive RAM (ReRAM), Magnetic RAM (MRAM), and Atomic Switch Network (ASN) are selected to review. Even if the memristor devices show such characteristics, the inherent error by their physical properties needs to be resolved. This paper suggests adopting an approximate computing approach to deal with the error without degrading the advantages of emerging memory devices.

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

2018 ◽  
Vol 8 (4) ◽  
pp. 34 ◽  
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.

Resistive Switching Memory based on Silver-doped Chitosan Thin Films

MRS Advances ◽  
2018 ◽  
Vol 3 (33) ◽  
pp. 1943-1948 ◽  
Author(s):  
C. Strobel ◽  
T. Sandner ◽  
S. Strehle
Keyword(s):  
Thin Films ◽  
Resistive Switching ◽  
Tin Oxide ◽  
Bottom Electrode ◽  
Low Cost ◽  
Memory Devices ◽  
Neuromorphic Computing ◽  
Resistive Switching Memory ◽  
Switching Behaviour ◽  
Switching Memory

AbstractMemristors represent an intriguing two-terminal device strategy potentially able to replace conventional memory devices as well as to support neuromorphic computing architectures. Here, we present the resistive switching behaviour of the sustainable and low-cost biopolymer chitosan, which can be extracted from natural chitin present for instance in crab exoskeletons. The biopolymer films were doped with Ag ions in varying concentrations and sandwiched between a bottom electrode such as fluorinated-tin-oxide and a silver top electrode. Silver-doped devices showed an overall promising resistive switching behaviour for doping concentrations between 0.5 to 1 wt% AgNO3. As bottom electrode fluorinated-tin-oxide, nickel, silver and titanium were studied and multiple write and erase cycles were recorded. However, the overall reproducibility and stability are still insufficient to support broader applicability.

scholarly journals Towards Neuromorphic Learning Machines using Emerging Memory Devices with Brain-like Energy Efficiency

2018 ◽  
Author(s):  
Vishal Saxena ◽  
Xinyu Wu ◽  
Ira Srivastava ◽  
Kehan Zhu
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Memory Devices ◽  
Cognitive Computing ◽  
Mixed Signal ◽  
Von Neumann ◽  
Neuromorphic Hardware ◽  
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 work, we review the challenges involved and present a pathway to realize ultra-low-power mixed-signal NeuSoC, from device arrays and circuits to spike-based deep learning algorithms, with ‘brain-like’ energy-efficiency.

Preface to the Special Issue on Beyond Moore: Resistive Switching Devices for Emerging Memory and Neuromorphic Computing

2021 ◽  
Vol 42 (1) ◽  
pp. 010101
Author(s):  
Yue Hao ◽  
Huaqiang Wu ◽  
Yuchao Yang ◽  
Qi Liu ◽  
Xiao Gong ◽  
...  
Keyword(s):  
Resistive Switching ◽  
Special Issue ◽  
Neuromorphic Computing ◽  
Emerging Memory ◽  
Switching Devices
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