Benchmarking Event-Driven Neuromorphic Architectures

Abstract

Neuromorphic architectures are represented by a broad class of hardware, with artificial neural network (ANN) architectures at one extreme and event-driven spiking architectures at another. Algorithms and applications efficiently processed by one neuromorphic architecture may be unsuitable for another, but it is challenging to compare various neuromorphic architectures among themselves and with traditional computer architectures. In this position paper, we take inspiration from architectural characterizations in scientific computing and motivate the need for neuromorphic architecture comparison techniques, outline relevant performance metrics and analysis tools, and describe cognitive workloads to meaningfully exercise neuromorphic architectures. Additionally, we propose a simulation-based framework for benchmarking a wide range of neuromorphic workloads. While this work is applicable to neuromorphic development in general, we focus on event-driven architectures, as they offer both unique performance characteristics and evaluation challenges.