A CMOS Spiking Neuron for Brain-Inspired Neural Networks with Resistive
Synapses and In-Situ Learning
release_jjnecwqj6zffbgp6kkntlzy6ya
by
Xinyu Wu, Vishal Saxena, Kehan Zhu, Sakkarapani Balagopal
2015
Abstract
Nanoscale 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 0.18 μm 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 0.01 mm^2 and has an energy-efficiency of 9.3
pJ/spike/synapse.
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