In‐Memory Mathematical Operations with Spin‐Orbit Torque Devices
Abstract: Analog arithmetic operations are the most fundamental mathematical operations used in image and signal processing as well as artificial intelligence (AI). In‐memory computing (IMC) offers a high performance and energy‐efficient computing paradigm. To date, in‐memory analog arithmetic operations with emerging nonvolatile devices are usually implemented using discrete components, which limits the scalability and blocks large scale integration. Here, a prototypical implementation of in‐memory analog arithmetic operations (summation, subtraction and multiplication) is experimentally demonstrated, based on in‐memory electrical current sensing units using spin‐orbit torque (SOT) devices. The proposed structures for analog arithmetic operations are smaller than the state‐of‐the‐art complementary metal oxide semiconductor (CMOS) counterparts by several orders of magnitude. Moreover, data to be processed and computing results can be locally stored, or the analog computing can be done in the nonvolatile SOT devices, which are exploited to experimentally implement the image edge detection and signal amplitude modulation with a simple structure. Furthermore, an artificial neural network (ANN) with SOT devices based synapses is constructed to realize pattern recognition with high accuracy of ≈95%.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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In‐Memory Mathematical Operations with Spin‐Orbit Torque Devices ; day:10 ; month:07 ; year:2022 ; extent:10
Advanced science ; (10.07.2022) (gesamt 10)
- Urheber
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Li, Ruofan
Song, Min
Guo, Zhe
Li, Shihao
Duan, Wei
Zhang, Shuai
Tian, Yufeng
Chen, Zhenjiang
Bao, Yi
Cui, Jinsong
Xu, Yan
Wang, Yaoyuan
Tong, Wei
Yuan, Zhe
Cui, Yan
Xi, Li
Feng, Dan
Yang, Xiaofei
Zou, Xuecheng
Hong, Jeongmin
You, Long
- DOI
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10.1002/advs.202202478
- URN
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urn:nbn:de:101:1-2022071115105034906988
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:20 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Li, Ruofan
- Song, Min
- Guo, Zhe
- Li, Shihao
- Duan, Wei
- Zhang, Shuai
- Tian, Yufeng
- Chen, Zhenjiang
- Bao, Yi
- Cui, Jinsong
- Xu, Yan
- Wang, Yaoyuan
- Tong, Wei
- Yuan, Zhe
- Cui, Yan
- Xi, Li
- Feng, Dan
- Yang, Xiaofei
- Zou, Xuecheng
- Hong, Jeongmin
- You, Long
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