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 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 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.

Versatile Memristor for Memory and Neuromorphic Computing

2022 ◽  
Author(s):  
Tao Guo ◽  
Kangqing Pan ◽  
Yixuan Jiao ◽  
Bai Sun ◽  
Cheng Du ◽  
...  
Keyword(s):  
Retention Time ◽  
High Density ◽  
Promising Candidate ◽  
Neuromorphic Computing

The memristor is a promising candidate to implement high-density memory and neuromorphic computing. Based on the retention time, memristors are classified into volatile and non-volatile types. However, a single memristor...

A Case for 3D Integrated System Design for Neuromorphic Computing and AI Applications

2020 ◽  
Vol 14 (04) ◽  
pp. 457-475
Author(s):  
Eren Kurshan ◽  
Hai Li ◽  
Mingoo Seok ◽  
Yuan Xie
Keyword(s):  
Human Brain ◽  
Scaling Laws ◽  
Three Dimensional ◽  
Implementation Process ◽  
Cost Effective ◽  
Integrated System ◽  
3D Integration ◽  
Neuromorphic Computing ◽  
Von Neumann ◽  
Manufacturing Technologies

Over the last decade, artificial intelligence (AI) has found many applications areas in the society. As AI solutions have become more sophistication and the use cases grew, they highlighted the need to address performance and energy efficiency challenges faced during the implementation process. To address these challenges, there has been growing interest in neuromorphic chips. Neuromorphic computing relies on non von Neumann architectures as well as novel devices, circuits and manufacturing technologies to mimic the human brain. Among such technologies, three-dimensional (3D) integration is an important enabler for AI hardware and the continuation of the scaling laws. In this paper, we overview the unique opportunities 3D integration provides in neuromorphic chip design, discuss the emerging opportunities in next generation neuromorphic architectures and review the obstacles. Neuromorphic architectures, which relied on the brain for inspiration and emulation purposes, face grand challenges due to the limited understanding of the functionality and the architecture of the human brain. Yet, high-levels of investments are dedicated to develop neuromorphic chips. We argue that 3D integration not only provides strategic advantages to the cost-effective and flexible design of neuromorphic chips, it may provide design flexibility in incorporating advanced capabilities to further benefit the designs in the future.

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