Abstract
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.
Highlights
Conventional computers have a processing unit and a memory, used to process and to store data, respectively
According to the so-called von Neumann architecture, processing and storage of data are separated [1]. This leads to the “von Neumann bottleneck”, meaning that data transport is nowadays slower than data processing and storage [2]
One of the ideas for data storage devices is based on domain wall motion, driven, e.g., by spin orbit torque in ferromagnet/heavy metal heterostructures [11,12]
Summary
Conventional computers have a processing unit and a memory, used to process and to store data, respectively. While separate buses for data and processing instructions or parallel computing might partly solve this problem, Backus suggested in 1978 to use an alternative program architecture in combination with new rules for state transition [3]. For this approach, new hardware would be supportive. Diverse approaches were made to create artificial neurons (i.e., computing elements) and synapses (i.e., memory elements) as the basic modules of cognitive hardware [9,10]. Molecules 2020, 25, 2550 of 14 create artificial neurons (i.e., computing elements) and synapses (i.e., memory elements) as the 2basic modules of cognitive hardware [9,10].
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