Ahsan Ragib, Wu Zezhi, Jalal Seyedeh Atiyeh Abbasi, Kapadia Rehan
Department of Electrical and Computer Engineering, University of Southern California, Los Angeles 90089-0001, United States.
ACS Omega. 2024 Apr 9;9(16):18062-18071. doi: 10.1021/acsomega.3c09936. eCollection 2024 Apr 23.
Here, we introduce an electronic circuit that mimics the functionality of a biological spiking neuron following the Fitzhugh-Nagumo (FN) model. The circuit consists of a tunnel diode that exhibits negative differential resistance (NDR) and an active inductive element implemented by a single MOSFET. The FN neuron converts a DC voltage excitation into voltage spikes analogous to biological action potentials. We predict an energy cost of 2 aJ/cycle through detailed simulation and modeling for these FN neurons. Such an FN neuron is CMOS compatible and enables ultralow power oscillatory and spiking neural network hardware. We demonstrate that FN neurons can be used for oscillator-based computing in a coupled oscillator network to form an oscillator Ising machine (OIM) that can solve computationally hard NP-complete max-cut problems while showing robustness toward process variations.
在此,我们介绍一种电子电路,它模仿遵循菲茨休 - 纳古莫(FN)模型的生物脉冲神经元的功能。该电路由一个呈现负微分电阻(NDR)的隧道二极管和一个由单个MOSFET实现的有源电感元件组成。FN神经元将直流电压激励转换为类似于生物动作电位的电压脉冲。通过对这些FN神经元进行详细的模拟和建模,我们预测每个周期的能量消耗为2阿焦耳。这种FN神经元与CMOS兼容,并能实现超低功耗的振荡和脉冲神经网络硬件。我们证明,FN神经元可用于耦合振荡器网络中基于振荡器的计算,以形成一个振荡器伊辛机(OIM),该机器能够解决计算困难的NP完全最大割问题,同时对工艺变化具有鲁棒性。