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Spiking Neural Networks (SNNs) are gaining widespread momentum in the fieldof neuromorphic computing. These network systems integrated with neurons andsynapses provide computational efficiency by mimicking the human brain. It isdesired to incorporate the biological neuronal dynamics, including complexspiking patterns which represent diverse brain activities within the neuralnetworks. Earlier hardware realization of neurons was (1) area intensivebecause of large capacitors in the circuit design, (2) neuronal spikingpatterns were demonstrated with clocked neurons at the device level. To achievemore realistic biological neuron spiking behavior, emerging memristive devicesare considered promising alternatives. In this paper, we propose, PrMnO3(PMO)-RRAM device-based neuron. The voltage-controlled electrothermal timescales ofthe compact PMO RRAM device replace the electrical timescales of charging alarge capacitor. The electrothermal timescale is used to implement anintegration block with multiple voltage-controlled timescales coupled with arefractory block to generate biological neuronal dynamics. Here, first, aVerilog-A implementation of the thermal device model is demonstrated, whichcaptures the current-temperature dynamics of the PMO device. Second, a drivingcircuitry is designed to mimic different spiking patterns of cortical neurons,including Intrinsic bursting (IB) and Chattering (CH). Third, a neuron circuitmodel is simulated, which includes the PMO RRAM device model and the drivingcircuitry to demonstrate the asynchronous neuron behavior. Finally, ahardware-software hybrid analysis is done in which the PMO RRAM device isexperimentally characterized to mimic neuron spiking dynamics. The workpresents a realizable and more biologically comparable hardware-efficientsolution for large-scale SNNs.

Exploiting the Electrothermal Timescale in PrMnO3 RRAM for a compact, clock-less neuron exhibiting biological spiking patterns