通过磁性材料中的物理存储计算进行视频识别

Q4 Physics and Astronomy New Physics: Sae Mulli Pub Date : 2023-10-10 DOI:10.3938/npsm.73.1155
Kaito Kobayashi, Y. Motome
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引用次数: 0

摘要

磁性材料中的非线性自旋动力学为实现物理水库计算提供了一条前景广阔的途径,而物理水库计算是受大脑启发而建立的最成功的信息处理框架之一。在本研究中,我们通过评估磁性物理库在视频识别任务中的性能,研究了磁性物理库的实用性。利用最近开发的时空并行化方案,我们的存储库实现了对先前提供的图像的准确分类。我们的研究结果为开发基于磁性物理水库计算的视觉传感器铺平了道路。
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Video Recognition by Physical Reservoir Computing in Magnetic Materials
Nonlinear spin dynamics in magnetic materials offers a promising avenue for implementing physical reservoir computing, one of the most accomplished brain-inspired frameworks for information processing. In this study, we investigate the practical utility of magnetic physical reservoirs by assessing their performance in a video recognition task. Leveraging a recently developed spatiotemporal parallelization scheme, our reservoir achieves accurate classifications of previously provided images. Our findings pave the way for the development of visual sensors based on the magnetic physical reservoir computing.
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来源期刊
New Physics: Sae Mulli
New Physics: Sae Mulli Physics and Astronomy-Physics and Astronomy (all)
CiteScore
0.40
自引率
0.00%
发文量
104
期刊介绍: New Physics: Sae Mulli monthly publishes peer-reviewed research papers and review papers which make significant, original and correct contributions to one of four categories of physics: 1) review of current physics topics, 2) applied physics(condensed matter, optics, etc.), 3) particle and nuclear physics, 4) other areas of physics (physics education, atomic and molecular physics, interdisciplinary, etc.). Both experimental and theoretical papers are accepted. Papers should be written in Korean or English. The review papers deliver the current physics topics of interests and the current instrumentation (both experimental and theoretical) knowledge at the university level so that even advanced high school students or undergraduate students can access easily. Papers based on successful and original student projects done at Korean local universities are specially welcome.
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