スポーツベットアイオーで「競馬」に賭ける方法や特徴を詳しく ...

International Affairs Students Current Students Alumni Faculty/Staff Careers--> TOHOKU UNIVERSITYCREATING GLOBAL EXCELLENCE Search 日本語 Contact Tohoku University --> About Facts & Figures Facilities Organization Chart History President's Message Top Global University Project Designated National University Global Network Promotional Videos Academics Undergraduate Graduate Courses in English Exchange Programs Summer Programs Double Degree Programs Academic Calendar Syllabus Admissions Undergraduate Admissions Graduate Admissions Fees and Expenses Financial Aid Research Feature Highlights Research Releases University Research News Research Institutes Visitor Research Center Research Profiles Academic Research Staff Campus Life International Support Office IT Services Facilities Dining & Shops Campus Bus Clubs & Circles News University News Research--> Arts & Culture Health & Sports Campus & Community Press Release--> International Visit Alumni Careers Events Exhibits Music Special Event Lecture Alumni--> Map & Directions Campus Maps & Bus--> Facilities Map--> TOHOKUUNIVERSITY About Academics Admissions Research Campus Life News Events International Affairs Students Current Students Alumni Faculty/Staff Promotional Videos Subscribe to our Newsletter Map & Directions Contact Jobs & Vacancies Emergency Information Site Map 日本語 Close Home Research News Giant Leap Towards Neuromorphic Devices: High-performance Spin-wave Reservoir Computing Research News Giant Leap Towards Neuromorphic Devices: High-performance Spin-wave Reservoir Computing 2024-03-06 A group of Tohoku University researchers has developed a theoretical model for a high-performance spin wave reservoir computing (RC) that utilizes spintronics technology. The breakthrough moves scientists closer to realizing energy-efficient, nanoscale computing with unparalleled computational power. Details of their findings were published in npj Spintronics on March 1, 2024. The brain is the ultimate computer and scientists are constantly striving to create neuromorphic devices that mimic the brain's processing capabilities, low power consumption, and its ability to adapt to neural networks. The development of neuromorphic computing is revolutionary, allowing scientists to explore nanoscale realms, GHz speed, with low energy consumption. In recent years, many advances in computational models inspired by the brain have been made. These artificial neural networks have demonstrated extraordinary performances in various tasks. However, current technologies are software-based; their computational speed, size, and energy consumption remain constrained by the properties of conventional electric computers. RC works via a fixed, randomly generated network called the 'reservoir.' The reservoir enables the memorization of past input information and its nonlinear transformation. This unique characteristic allows for the integration of physical systems, such as magnetization dynamics, to perform various tasks for sequential data, like time-series forecasting and speech recognition. Some have proposed spintronics as a means to realize high-performance devices. But devices produced so far have failed to live up to expectations. In particular, they have failed to achieve high performance at nanoscales with GHz speed. "Our study proposed a physical RC that harnessed propagating spin waves," says Natsuhiko Yoshinaga, co-author of the paper and associate professor at the Advanced Institute for Materials Research (WPI-AIMR). "The theoretical framework we developed utilized response functions that link input signals to propagating spin dynamics. This theoretical model elucidated the mechanism behind the high performance of spin wave RC, highlighting the scaling relationship between wave speed and system size to optimize the effectiveness of virtual nodes." A physical reservoir computer performs a task to transform input data to output data, such as time-series prediction. Magnetic thin film was used fofr the reservoir part. Information of the input is carried by spin waves and propagated to the output node (shown in blue cylinders in the bottom figure) corresponding to the nodes in the reservoir (shown in yellow in the top figure). ©Springer Nature Limited Crucially, Yoshinaga and his colleagues helped clarify the mechanism for high-performance reservoir computing. In doing so, they harnessed various subfields, namely condensed matter physics and mathematical modeling. "By employing the unique properties of spintronics technology, we have potentially paved the way for a new era of intelligent computing, leading us closer to realizing a physical device that can be put to use in weather forecasts and speech recognition" adds Yoshinaga. Publication Details: Title: Universal scaling between wave speed and size enables nanoscale high-performance reservoir computing based on propagating spin-wavesAuthors: S. Iihama, Y. Koike, S. Mizukami, and N. YoshinagaJournal: npj SpintronicsDOI: 10.1038/s44306-024-00008-5 Press release in Japanese Contact: Natsuhiko Yoshinaga,Advanced Institute for Materials Research (WPI-AIMR)Email: yoshinagatohoku.ac.jpWebsite: https://www.wpi-aimr.tohoku.ac.jp/~yoshinaga/index.html --> Archives 2014&＃24180; 2015&＃24180; 2016&＃24180; 2017&＃24180; 2018&＃24180; 2019&＃24180; 2020&＃24180; 2021&＃24180; 2022&＃24180; 2023&＃24180; 2024&＃24180; Page Top About Tohoku University Academics Admissions Research Campus Life News Events International Affairs Students Alumni Promotional Videos Subscribe to our Newsletter Map & Directions Contact Tohoku University Jobs & Vacancies Emergency Information Site Map Privacy Policy Media Enquiries Parent & Family Support Public Facilities Contact Tohoku University