一种采用实时传感方法的节能神经形态计算系统

H. Farkhani, Mohammad Tohidi, Sadaf Farkhani, J. K. Madsen, F. Moradi
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引用次数: 3

摘要

在基于自旋电子学的神经形态计算系统(NCS)中,利用磁隧道结(MTJ)中磁矩的切换来模拟神经元放电。然而,MTJ的随机切换行为和过程变化效应导致了额外的刺激时间。这将导致ncs的额外能耗和延迟。本文提出了一种新的实时传感电路,用于跟踪MTJ状态,并在MTJ切换后立即终止刺激阶段。这导致了NCS的能耗和延迟的显著下降。采用65纳米CMOS技术和40纳米MTJ技术的仿真结果证实,与典型的NCS相比,基于rts的NCS的能耗提高了50%。此外,利用RTS电路将NCS的整体速度提高了2.75倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An energy efficient neuromorphic computing system using real time sensing method
In spintronic-based neuromorphic computing systems (NCS), the switching of magnetic moment in a magnetic tunnel junction (MTJ) is used to mimic neuron firing. However, the stochastic switching behavior of the MTJ and process variations effect leads to extra stimulation time. This leads to extra energy consumption and delay of such NCSs. In this paper, a new real-time sensing (RTS) circuit is proposed to track the MTJ state and terminate stimulation phase immediately after MTJ switching. This leads to significant degradation in energy consumption and delay of NCS. The simulation results using a 65-nm CMOS technology and a 40-nm MTJ technology confirm that the energy consumption of a RTS-based NCS is improved by 50% in comparison with a typical NCS. Moreover, utilizing RTS circuit improves the overall speed of an NCS by 2.75x.
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