Non-Volatile In-Memory Computing by Spintronics
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 tun...
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| Main Authors: | , , |
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| Other Authors: | |
| Format: | Book |
| Language: | English |
| Published: |
Morgan & Claypool
2017
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/85458 http://hdl.handle.net/10220/43703 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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