Non-Volatile In-Memory Computing by Spintronics - Hao Yu,Leibin Ni,Yuhao Wang
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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 presented. Next, we show that the spintronics could be a candidate for f ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | Hao Yu, Leibin Ni, Yuhao Wang |
|---|---|
| Izdevējs | Springer Nature Switzerland |
| Series | Synthesis Lectures on Emerging Engineering Technologies |
| Izlaides gads | 2016 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783031009044 |