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Zhengke Yang on Muck Rack

Zhengke Yang

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A lossless and fully parallel spintronic compute-in-memory macro for artificial intelligence chips - Nature Electronics

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By Zheng Chai, Zhen Kong, Xue Zhou, Feichi Zhou, Yida Li, Humiao Li, Xihui Yuan, Zhengke Yang
| Nature Verified
Abstract Non-volatile compute-in-memory macros can reduce data transfer between processing and memory units, providing fast and energy-efficient artificial intelligence computations. However, the non-volatile compute-in-memory architecture typically relies on analogue computing, which is limited in terms of accuracy, scalability and robustness. Here we report a 64-kb non-volatile digital compute-in-memory macro based on 40-nm spin-transfer torque magnetic random-access memory technology.
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