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., Pr 0.7 Ca 0.3 MnO 3 (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.