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Wir sind gerne für Sie daExa-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 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.
Produkteigenschaften
- Artikelnummer: 9783031009044
- Medium: Buch
- ISBN: 978-3-031-00904-4
- Verlag: Springer
- Erscheinungstermin: 02.12.2016
- Sprache(n): Englisch
- Auflage: Erscheinungsjahr 2016
- Serie: Synthesis Lectures on Emerging Engineering Technologies
- Produktform: Kartoniert, Paperback
- Gewicht: 320 g
- Seiten: 147
- Format (B x H x T): 191 x 235 x 10 mm
- Ausgabetyp: Kein, Unbekannt