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Benchmarking neuromorphic systems with Nengo.

Trevor Bekolay¹, Terrence C Stewart¹, Chris Eliasmith¹

¹Centre for Theoretical Neuroscience, University of Waterloo Waterloo, ON, Canada.

Frontiers in Neuroscience

|November 6, 2015

Summary

This summary is machine-generated.

Nengo software enables neural model simulation across diverse hardware, including neuromorphic systems. Its extensive test suite efficiently benchmarks hardware performance and simulation speed for unbiased evaluation.

Keywords:

Nengo benchmarking large-scale neural networks neuromorphic hardware spiking neural networks

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Area of Science:

Computational Neuroscience
Software Engineering

Background:

Nengo is a versatile software package for large-scale neural model design and simulation.
Its architecture supports cross-platform compatibility, enabling models to run on various backends like GPUs and neuromorphic hardware with minimal changes.

Purpose of the Study:

To propose and validate the use of Nengo's comprehensive test suite for benchmarking neuromorphic hardware.
To assess the efficiency, unbiased nature, and future-proofing of Nengo for hardware performance evaluation.

Main Methods:

Implementing four distinct benchmark models within the Nengo framework.
Utilizing Nengo's capability to collect performance metrics across multiple backends.
Comparing functional performance and simulation speed across different hardware platforms.

Main Results:

Nengo successfully collected performance metrics across five different backends.
The benchmark models identified specific scenarios where certain backends demonstrated superior accuracy or speed.
Demonstrated the feasibility of using Nengo's test suite for comparative hardware analysis.

Conclusions:

Nengo's test suite provides an efficient and unbiased method for benchmarking neuromorphic hardware.
The software facilitates the assessment of functional performance and simulation speed across diverse computational platforms.
Nengo is a valuable tool for evaluating and comparing the capabilities of emerging neuromorphic technologies.