Open AccessA Coupled Spintronics Neuromorphic Approach for High‐Performance Reservoir Computing
Author(s) -
Akashi Nozomi,
Kuniyoshi Yasuo,
Tsunegi Sumito,
Taniguchi Tomohiro,
Nishida Mitsuhiro,
Sakurai Ryo,
Wakao Yasumichi,
Kawashima Kenji,
Nakajima Kohei
Publication year - 2022
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202200123
Subject(s) - neuromorphic engineering , reservoir computing , spintronics , computer science , computer architecture , exploit , edge computing , computer engineering , artificial intelligence , enhanced data rates for gsm evolution , artificial neural network , physics , computer security , quantum mechanics , ferromagnetism , recurrent neural network
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.