Nano-scale resistive memories are expected to fuel dense integration of electronic synapses for large-scale neuromorphic system. To realize such a brain-inspired computing chip, a compact CMOS spiking neuron that performs in-situ learning and computing while driving a large number of resistive synapses is desired. This work presents a novel leaky integrate-and-fire neuron design which implements the dual-mode operation of current integration and synaptic drive, with a single opamp and enables in-situ learning with crossbar resistive synapses. The proposed design was implemented in a 0.18μm CMOS technology. Measurements show neuron’s ability to drive a thousand resistive synapses, and demonstrate an in-situ associative learning. The neuron circuit occupies a small area of 0.01mm2 and has an energy-efficiency of 9.3pJ/spike/synapse.
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Wu, Xinyu; Saxena, Vishal; Zhu, Kehan; and Balagopal, Sakkarapani. (2015). "A CMOS Spiking Neuron for Brain-Inspired Neural Networks with Resistive Synapses and In-Situ Learning". IEEE Transactions on Circuits and Systems II: Express Briefs, 62(11), 1088-1092. http://dx.doi.org/10.1109/TCSII.2015.2456372