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Abstract
En 中文
Neuromorphic computing is a broad category of non–von Neumann architectures that mimic biological nervous systems using hardware. Current research shows that this class of computing can execute data classification algorithms using only a tiny fraction of the power conventional CPUs require. This raises the larger research question:
How might neuromorphic computing be used to improve application performance, power consumption, and overall system reliability of future supercomputers?
To address this question, an open-source neuromorphic processor architecture simulator called
NeMo
is being developed. This effort will enable the design space exploration of potential heterogeneous compute systems that combine traditional CPUs, GPUs, and neuromorphic hardware. This article examines the design, implementation, and performance of
NeMo
. Demonstration of
NeMo
’s efficient execution using 2,048 nodes of an IBM Blue Gene/Q system, modeling 8,388,608 neuromorphic processing cores is reported. The peak performance of
NeMo
is just over ten billion events-per-second when operating at this scale.
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