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Magnetic skyrmions are topological spin textures, with great potential for energy-saving next generation devices such as non-volatile memory devices. Recently, the application of skyrmions to neuromorphic devices, including physical reservoir computing devices, has attracted much attention. In this article, we introduce the recent progress in research on skyrmion-based physical reservoir computing, mainly focusing on our results. We recently demonstrated physical reservoir computing using the nonlinear response originating from the magnetic-field induced dynamics of skyrmions formed in ultrathin multilayer films. We succeeded in waveform recognition and handwritten digit recognition. Notably, there is a positive correlation between the recognition accuracy and the number of skyrmions in the devices. In addition to our results, skyrmion-based reservoir computing devices have also been reported by other groups, highlighting the high potential of magnetic skyrmions for physical reservoir computing. [ABSTRACT FROM AUTHOR]

Physical reservoir computing is a framework that enables energy‐efficient information processing by using physical systems. Nonlinear dynamics in physical systems provide a computational capability that is unique to reservoirs. It is, however, difficult to find an appropriate task for a reservoir because of the complexity of nonlinear information processing. The information processing capacity has recently been used to clarify systematically the tasks that are solved by reservoirs; it quantifies the memory capacity of reservoirs in accordance with the order of nonlinearity. Herein, an experimental evaluation of the information processing capacity of a spintronic oscillator consisting of nanostructured ferromagnets is reported. The spintronic reservoir state is electrically manipulated by adding a delayed‐feedback circuit. The total capacity reaches a maximum of 5.6 at the edge of the echo state property. A trade‐off between the linear and nonlinear components of the capacity is also found. The result can be used to better understand the nonlinear information processing in reservoirs and to find good matches between reservoirs and tasks. As an example, a function‐approximation task is performed and it is found that it can be efficiently solved when the reservoir state is appropriately tuned so that its information processing capacity matches that of the task. [ABSTRACT FROM AUTHOR]

Physical reservoir computing is a framework that enables energy‐efficient information processing by using physical systems. Nonlinear dynamics in physical systems provide a computational capability that is unique to reservoirs. It is, however, difficult to find an appropriate task for a reservoir because of the complexity of nonlinear information processing. The information processing capacity has recently been used to clarify systematically the tasks that are solved by reservoirs; it quantifies the memory capacity of reservoirs in accordance with the order of nonlinearity. Herein, an experimental evaluation of the information processing capacity of a spintronic oscillator consisting of nanostructured ferromagnets is reported. The spintronic reservoir state is electrically manipulated by adding a delayed‐feedback circuit. The total capacity reaches a maximum of 5.6 at the edge of the echo state property. A trade‐off between the linear and nonlinear components of the capacity is also found. The result can be used to better understand the nonlinear information processing in reservoirs and to find good matches between reservoirs and tasks. As an example, a function‐approximation task is performed and it is found that it can be efficiently solved when the reservoir state is appropriately tuned so that its information processing capacity matches that of the task.

Physical reservoir computing is a computational paradigm that enables spatiotemporal pattern recognition to be performed directly in matter. The use of physical matter leads the way toward energy‐efficient devices capable of solving machine learning problems without having to build a system of millions of interconnected neurons. Proposed herein is a high‐performance "skyrmion mixture reservoir" that implements the reservoir computing model with multidimensional inputs. This implementation solves spoken digit classification tasks with an overall model accuracy of 97.4% and a < 1% word error ratethe best performance ever reported for in materio reservoir computers. Due to the quality of the results and the low‐power properties of magnetic texture reservoirs, it is evident that skyrmion fabrics are a compelling candidate for reservoir computing. [ABSTRACT FROM AUTHOR]

Physical reservoir computing is a computational paradigm that enables spatiotemporal pattern recognition to be performed directly in matter. The use of physical matter leads the way toward energy‐efficient devices capable of solving machine learning problems without having to build a system of millions of interconnected neurons. Proposed herein is a high‐performance “skyrmion mixture reservoir” that implements the reservoir computing model with multidimensional inputs. This implementation solves spoken digit classification tasks with an overall model accuracy of 97.4% and a

The rapid development in the field of artificial intelligence has increased the demand for neuromorphic computing hardware and its information‐processing capability. A spintronics device is a promising candidate for neuromorphic computing hardware and can be used in extreme environments due to its high resistance to radiation. Improving the information‐processing capability of neuromorphic computing is an important challenge for implementation. Herein, a novel neuromorphic computing framework using spintronics devices is proposed. This framework is called coupled spintronics reservoir (CSR) computing and exploits the high‐dimensional dynamics of coupled spin‐torque oscillators as a computational resource. The relationships among various bifurcations of the CSR and its information‐processing capabilities through numerical experiments are analyzed and it is found that certain configurations of the CSR boost the information‐processing capability of the spintronics reservoir toward or even beyond the standard level of machine learning networks. The effectiveness of our approach is demonstrated through conventional machine learning benchmarks and edge computing in real physical experiments using pneumatic artificial muscle‐based wearables, which assist human operations in various environments. This study significantly advances the availability of neuromorphic computing for practical uses. [ABSTRACT FROM AUTHOR]

The rapid development in the field of artificial intelligence has increased the demand for neuromorphic computing hardware and its information‐processing capability. A spintronics device is a promising candidate for neuromorphic computing hardware and can be used in extreme environments due to its high resistance to radiation. Improving the information‐processing capability of neuromorphic computing is an important challenge for implementation. Herein, a novel neuromorphic computing framework using spintronics devices is proposed. This framework is called coupled spintronics reservoir (CSR) computing and exploits the high‐dimensional dynamics of coupled spin‐torque oscillators as a computational resource. The relationships among various bifurcations of the CSR and its information‐processing capabilities through numerical experiments are analyzed and it is found that certain configurations of the CSR boost the information‐processing capability of the spintronics reservoir toward or even beyond the standard level of machine learning networks. The effectiveness of our approach is demonstrated through conventional machine learning benchmarks and edge computing in real physical experiments using pneumatic artificial muscle‐based wearables, which assist human operations in various environments. This study significantly advances the availability of neuromorphic computing for practical uses.