title: Benchmarking neuromorphic systems with Nengo.
abstract: Nengo is a software package for designing and simulating large-scale neural models. Nengo is architected such that the same Nengo model can be simulated on any of several Nengo backends with few to no modifications. Backends translate a model to specific platforms, which include GPUs and neuromorphic hardware. Nengo also contains a large test suite that can be run with any backend and focuses primarily on functional performance. We propose that Nengo's large test suite can be used to benchmark neuromorphic hardware's functional performance and simulation speed in an efficient, unbiased, and future-proof manner. We implement four benchmark models and show that Nengo can collect metrics across five different backends that identify situations in which some backends perform more accurately or quickly.
authors.affiliation: Centre for Theoretical Neuroscience, University of Waterloo Waterloo, ON, Canada.
authors.forename: Trevor
authors.initials: T
authors.lastname: Bekolay
authors.affiliation: Centre for Theoretical Neuroscience, University of Waterloo Waterloo, ON, Canada.
authors.forename: Terrence C
authors.initials: TC
authors.lastname: Stewart
authors.affiliation: Centre for Theoretical Neuroscience, University of Waterloo Waterloo, ON, Canada.
authors.forename: Chris
authors.initials: C
authors.lastname: Eliasmith
keywords: Nengo; benchmarking; large-scale neural networks; neuromorphic hardware; spiking neural networks