基于自旋的神经网络的认知计算

M. Sharad, C. Augustine, G. Panagopoulos, K. Roy
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引用次数: 20

摘要

采用横向自旋阀(LSV)对阶跃传递函数神经元进行建模,并提出了其在低功耗神经网络硬件中的应用。在这种网络中,计算任务由纳米磁铁、金属通道和可编程导电元件完成,它们构成神经元-突触单元,并在~ 20mv的终端电压下工作。CMOS晶体管以时钟、功率门控和神经元间信号的形式提供外围支持。认知和布尔计算应用的模拟显示,在相同的技术节点上,与传统CMOS设计相比,功耗提高了94%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cognitive computing with spin-based neural networks
We model a step transfer function neuron with lateral spin valve (LSV) and propose its application in low power neural network hardware. The computational task in such a network is performed by nano-magnets, metal channels and programmable conductive elements, that constitute the neuron-synapse units and operate at a terminal voltage of ~20 mV. CMOS transistors provide peripheral support in the form of clocking, power gating and inter-neuron signaling. Simulations for cognitive as well as Boolean computation applications show more than 94% improvement in power consumption as compared to a conventional CMOS design at the same technology node.
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