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Integrate-and-fire neuron circuit using positive feedback field effect transistor for low power operation

机译:使用正反馈场效应晶体管的集成发射神经元电路,可实现低功耗运行

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In this work, we fabricated a dual gate positive feedback field-effect transistor (FBFET) integrated with CMOS. We investigated the DC and transient characteristics of the FBFET. The fabricated FBFET has an extremely low sub-threshold slope of less than 2.3 mV/dec and low off-current. We also propose an analog integrated-and-fire neuron circuit incorporating a FBFET, which significantly reduces the power dissipation of hardware neural networks. In a conventional neuron circuit using a membrane capacitor to integrate input pulses, most of the energy is consumed by the first inverter stage connected to the capacitor. Since the membrane capacitor is charged slowly compared to digital logic, a large amount of short-circuit current flows between V-dd and ground in the first inverter during this period. In the proposed neuron circuit, the short-circuit current is significantly suppressed by adopting a FBFET in the inverter. Through TCAD mixed mode simulation of the device and the circuit, we compare the energy consumption of a conventional and the proposed neuron circuits. In a single neuron circuit with microsecond duration pulses, 58% of the energy consumption is reduced by incorporating a FBFET. We performed SPICE compact modeling of FBFET, and its parameters were fitted to match the measurement results of the fabricated FBFET. Then, we conducted a circuit simulation to verify the operating neural networks. We implemented a single layer spiking neural network (SNN) that had resistive synaptic devices. In the SNN simulation, approximately 94% of the average power consumption of all output neurons was reduced. Published by AIP Publishing.
机译:在这项工作中,我们制造了集成有CMOS的双栅极正反馈场效应晶体管(FBFET)。我们研究了FBFET的直流和瞬态特性。所制造的FBFET具有低于2.3 mV / dec的极低亚阈值斜率和低截止电流。我们还提出了一种包含FBFET的模拟集成点火神经元电路,该电路可大大降低硬件神经网络的功耗。在使用膜电容器来积分输入脉冲的常规神经元电路中,大部分能量被连接到电容器的第一反相器级消耗。由于与数字逻辑相比,薄膜电容器的充电速度较慢,因此在此期间,大量短路电流在第一逆变器的V-dd与地之间流动。在提出的神经元电路中,通过在逆变器中采用FBFET可以显着抑制短路电流。通过对设备和电路进行TCAD混合模式仿真,我们比较了常规神经元电路和拟议神经元电路的能耗。在具有微秒持续时间脉冲的单个神经元电路中,通过集成FBFET可以减少58%的能量消耗。我们对FBFET进行了SPICE紧凑建模,并对其参数进行了拟合以匹配所制造FBFET的测量结果。然后,我们进行了电路仿真以验证运行神经网络。我们实现了具有阻力突触设备的单层尖峰神经网络(SNN)。在SNN模拟中,所有输出神经元的平均功耗降低了约94%。由AIP Publishing发布。

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