NeMo: A Massively Parallel Discrete-Event Simulation Model for Neuromorphic Architectures

摘要

Neuromorphic computing is a non-von Neumann architecture that mimics how the brain performs neural network types of computation in real hardware. It has been shown that this class of computing can execute data classification algorithms using only a tiny fraction of the power a conventional CPU would use to execute this algorithm. This raises the larger research question: how might neuromorphic computing be used to improve the 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 hybrid CPU, GPU, and neuromorphic systems. The key focus of this paper is on the design, implementation and performance of NeMo. Demonstration of NeMo's efficient execution on 1024 nodes of an IBM Blue Gene/Q system for a 65,536 neuromorphic processing core model is reported. The peak performance of NeMo is just over two billion events-per-second when operating at this scale.