Neuromorphic computing with stochastic spintronic devices (Conference Presentation)

Spintronics X ◽  
2017 ◽  
Author(s):  
Damien Querlioz ◽  
Adrien F. Vincent ◽  
Alice Mizrahi ◽  
Damir Vodenicarevic ◽  
Nicolas Locatelli ◽  
...  
Keyword(s):  
Neuromorphic Computing ◽  
Spintronic Devices

scholarly journals Three Artificial Spintronic Leaky Integrate-and-Fire Neurons

SPIN ◽  
2020 ◽  
Vol 10 (02) ◽  
pp. 2040003
Author(s):  
Wesley H. Brigner ◽  
Xuan Hu ◽  
Naimul Hassan ◽  
Lucian Jiang-Wei ◽  
Christopher H. Bennett ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Domain Wall ◽  
Dipolar Coupling ◽  
Neuromorphic Computing ◽  
Coupling Field ◽  
Spintronic Devices ◽  
Integrate And Fire ◽  
The Third ◽  
Significant Interest

Due to their nonvolatility and intrinsic current integration capabilities, spintronic devices that rely on domain wall (DW) motion through a free ferromagnetic track have garnered significant interest in the field of neuromorphic computing. Although a number of such devices have already been proposed, they require the use of external circuitry to implement several important neuronal behaviors. As such, they are likely to result in either a decrease in energy efficiency, an increase in fabrication complexity, or even both. To resolve this issue, we have proposed three individual neurons that are capable of performing these functionalities without the use of any external circuitry. To implement leaking, the first neuron uses a dipolar coupling field, the second uses an anisotropy gradient and the third uses shape variations of the DW track.

scholarly journals Spintronic devices for neuromorphic computing

2020 ◽  
Vol 63 (7) ◽  
Author(s):  
YaJun Zhang ◽  
Qi Zheng ◽  
XiaoRui Zhu ◽  
Zhe Yuan ◽  
Ke Xia
Keyword(s):  
Neuromorphic Computing ◽  
Spintronic Devices

scholarly journals Magnetic Elements for Neuromorphic Computing

Molecules ◽  
2020 ◽  
Vol 25 (11) ◽  
pp. 2550 ◽  
Author(s):  
Tomasz Blachowicz ◽  
Andrea Ehrmann
Keyword(s):  
Magnetic Materials ◽  
Data Storage ◽  
Energy Efficient ◽  
Domain Walls ◽  
Magnetic Nanostructures ◽  
Data Transport ◽  
Neuromorphic Computing ◽  
Von Neumann ◽  
Spintronic Devices ◽  
Magnetic Elements

Neuromorphic computing is assumed to be significantly more energy efficient than, and at the same time expected to outperform, conventional computers in several applications, such as data classification, since it overcomes the so-called von Neumann bottleneck. Artificial synapses and neurons can be implemented into conventional hardware using new software, but also be created by diverse spintronic devices and other elements to completely avoid the disadvantages of recent hardware architecture. Here, we report on diverse approaches to implement neuromorphic functionalities in novel hardware using magnetic elements, published during the last years. Magnetic elements play an important role in neuromorphic computing. While other approaches, such as optical and conductive elements, are also under investigation in many groups, magnetic nanostructures and generally magnetic materials offer large advantages, especially in terms of data storage, but they can also unambiguously be used for data transport, e.g., by propagation of skyrmions or domain walls. This review underlines the possible applications of magnetic materials and nanostructures in neuromorphic systems.

scholarly journals Integrated neuromorphic computing networks by artificial spin synapses and spin neurons

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Seungmo Yang ◽  
Jeonghun Shin ◽  
Taeyoon Kim ◽  
Kyoung-Woong Moon ◽  
Jaewook Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Hardware Implementation ◽  
Experimental Studies ◽  
Proof Of Concept ◽  
Stripe Domain ◽  
Network Systems ◽  
Neuromorphic Computing ◽  
Spintronic Devices ◽  
Low Energy Consumption ◽  
Neural Network Hardware

AbstractOne long-standing goal in the emerging neuromorphic field is to create a reliable neural network hardware implementation that has low energy consumption, while providing massively parallel computation. Although diverse oxide-based devices have made significant progress as artificial synaptic and neuronal components, these devices still need further optimization regarding linearity, symmetry, and stability. Here, we present a proof-of-concept experiment for integrated neuromorphic computing networks by utilizing spintronics-based synapse (spin-S) and neuron (spin-N) devices, along with linear and symmetric weight responses for spin-S using a stripe domain and activation functions for spin-N. An integrated neural network of electrically connected spin-S and spin-N successfully proves the integration function for a simple pattern classification task. We simulate a spin-N network using the extracted device characteristics and demonstrate a high classification accuracy (over 93%) for the spin-S and spin-N optimization without the assistance of additional software or circuits required in previous reports. These experimental studies provide a new path toward establishing more compact and efficient neural network systems with optimized multifunctional spintronic devices.

Non-Boolean Computing with Spintronic Devices

2018 ◽  
Author(s):  
Roxy Kawsher A. ◽  
Sanjukta Bhanja
Keyword(s):  
Spintronic Devices

Foundations of Neuromorphic Computing

2013 ◽  
Author(s):  
Clare Thiem ◽  
Bryant Wysocki ◽  
Morgan Bishop ◽  
Nathan McDonald ◽  
James Bohl
Keyword(s):  
Neuromorphic Computing

Neuromorphic Computing for Very Large Test and Evaluation Data Analysis

2014 ◽  
Author(s):  
Bryant Wysocki ◽  
Nathan McDonald ◽  
Clare Thiem ◽  
Thomas Renz ◽  
James Bohl
Keyword(s):  
Data Analysis ◽  
Neuromorphic Computing ◽  
Evaluation Data ◽  
Test And Evaluation

Polariton Devices

2017 ◽  
Author(s):  
Alexey V. Kavokin ◽  
Jeremy J. Baumberg ◽  
Guillaume Malpuech ◽  
Fabrice P. Laussy
Keyword(s):  
Semiconductor Devices ◽  
Logic Gates ◽  
Light Sources ◽  
Coherent Light ◽  
Final State ◽  
Spintronic Devices ◽  
Exciton Polaritons ◽  
Terahertz Lasers ◽  
Coherence Lengths ◽  
Stimulation Of

Polariton devices offer multiple advantages compared to conventional semiconductor devices. The bosonic nature of exciton polaritons offers opportunity of realisation of polariton lasers: coherent light sources based on bosonic condensates of polaritons. The final state stimulation of any transition feeding a polariton condensate has been used in many proposals such as for terahertz lasers based on polariton lasers. Furthermore, large coherence lengths of exciton-polaritons in microcavities open the way to realisation of polariton transport devices including transistors and logic gates. Being bosonic spin carriers, exciton-polaritons may be used in spintronic devices and polarisation switches. This chapter offers an overview on the existing proposals for polariton devices.

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