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PCMO RRAM for Integrate-and-Fire Neuron in Spiking Neural Networks

Resistance random access memories (RRAM) or memristors with an analog change of conductance are widely explored as an artificial synapse, e.g., Pr0.7Ca0.3MnO3 (PCMO) RRAM-based synapses. In addition to synapses, scaled neurons are essential to enable a neuromorphic hardware. In this letter, we propose a PCMO RRAM for integrate and fire (IF) neuron. The analog conductance increase during SET process enables integration function. Upon exceeding a conductance threshold (i.e., fire) during a READ operation, a hard RESET is performed to reduce the conductance. The SET, READ, and RESET are performed in different phases of a clock to enable a PCMO for IF neuron. The availability of a non-volatile PCMO-based synapse makes PCMO for IF neuron attractive. Finally, PCMO-based neuron in spiking neural network yields software-equivalent classification accuracy as demonstrated on standard Fischer’s Iris flower data set.