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Record Nr. |
UNINA9910808511703321 |
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Autore |
Yu Hao |
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Titolo |
Non-Volatile In-Memory Computing by Spintronics [[electronic resource] /] / by Hao Yu, Leibin Ni, Yuhao Wang |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (XIII, 147 p.) |
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Collana |
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Synthesis Lectures on Emerging Engineering Technologies, , 2381-1439 |
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Disciplina |
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Soggetti |
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Engineering |
Electrical engineering |
Electronic circuits |
Computers |
Materials science |
Surfaces (Technology) |
Thin films |
Technology and Engineering |
Electrical and Electronic Engineering |
Electronic Circuits and Systems |
Computer Hardware |
Materials Science |
Surfaces, Interfaces and Thin Film |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Preface -- Acknowledgments -- Introduction -- Non-volatile Spintronic Device and Circuit -- In-memory Data Encryption -- In-memory Data Analytics -- Authors' Biographies . |
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Sommario/riassunto |
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Exa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main bottleneck is from memory, we aim to develop an energy-efficient in-memory computing platform in this book. First, the models of spin-transfer torque magnetic tunnel junction and racetrack memory are |
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presented. Next, we show that the spintronics could be a candidate for future data-oriented computing for storage, logic, and interconnect. As a result, by utilizing spintronics, in-memory-based computing has been applied for data encryption and machine learning. The implementations of in-memory AES, Simon cipher, as well as interconnect are explained in details. In addition, in-memory-based machine learning and face recognition are also illustrated in this book. |
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