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 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.
| Autorius: | Hao Yu, Yuhao Wang, Leibin Ni, |
| Serija: | Synthesis Lectures on Emerging Engineering Technologies |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2016 |
| Knygos puslapių skaičius: | 164 |
| ISBN-10: | 3031009045 |
| ISBN-13: | 9783031009044 |
| Formatas: | Knyga minkštu viršeliu |
| Kalba: | Anglų |
| Žanras: | Electronics: circuits and components |
Parašykite atsiliepimą apie „Non-Volatile In-Memory Computing by Spintronics“
