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Nabil Imam, Kyle Wecker, Jonathan Tse, Robert Karmazin, and Rajit Manohar
We present an implementation of a digital neuron
in silicon, using delay-insensitive asynchronous circuits. Our
design numerically solves the Izhikevich equations with a fixed-point
number representation, resulting in a compact and energy-efficient
neuron with a variety of dynamical characteristics.
A digital implementation results in stable, reliable and highly
programmable circuits, while an asynchronous design style leads
to energy-efficient clockless neurons and their networks that
mimic the event-driven nature of biological nervous systems. In
65nm CMOS technology at 1 V operating voltage and a 16-bit word length, our neuron can update its state 11,600 times
per millisecond while consuming 0.5 nJ per update. The design
occupies 29,500μm2 and can be used to construct large-scale
neuromorphic systems. Our neuron exhibits the full reportoire
of spiking features seen in biological neurons, resulting in a range
of computational properties that can be used in artificial systems
running neural-inspired algorithms, in neural prosthetic devices,
and in accelerated brain simulations
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