Stochastic Spiking Neural Networks Enabled by Magnetic Tunnel Junctions: From Nontelegraphic to Telegraphic Switching Regimes
Stochastic Spiking Neural Networks Enabled by Magnetic Tunnel Junctions: From Nontelegraphic to Telegraphic Switching Regimes
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由磁隧道结实现的随机尖峰神经网络：从非电报到电报的切换机制
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
K. Roy
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文献类型：
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作者：
C. Liyanagedera;Abhronil Sengupta;Akhilesh R. Jaiswal;K. Roy
围绕纳米电子组件建立的人工神经网络是实现紧凑，节能认知智能的一种手段。作者使用纳米磁体的固有设备物理学来模仿神经网络的计算原语，从而减少了基础硬件的能量和区域要求。他们分析了具有不同屏障高度的磁体的随机神经形态计算平台的性能，并显示了如何随着磁铁扩展到超级磁性状态时如何修改核心网络架构。
Artificial neural networks built around nanoelectronic components are a means to realizing compact, energy-efficient cognitive intelligence. The authors use the inherent device physics of nanomagnets to emulate the computational primitives of a neural network, reducing the energy and area requirements of the underlying hardware. They analyze the performance of stochastic neuromorphic computing platforms with magnets of different barrier heights, and show how the core network architecture must be modified as the magnets scale down to the superparamagnetic regime.