Neural Spiking Dynamics in Asynchronous Digital Circuits

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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