NeuroPack: An Algorithm-level Python-based Simulator for Memristor-empowered Neuro-inspired Computing

TL;DR

NeuroPack is a versatile Python-based simulation platform that enables the design and analysis of memristor-based neuro-inspired computing architectures, supporting various models for neurons, learning rules, and memristors.

Contribution

It introduces a modular, hierarchical simulation environment that can predict memristor state changes and neural network behavior across diverse design choices.

Findings

Successfully simulated handwritten digit classification with MNIST dataset.

Supported multiple neuron and memristor models within a unified platform.

Demonstrated the platform's versatility for designing memristor-based neural architectures.

Abstract

Emerging two terminal nanoscale memory devices, known as memristors, have over the past decade demonstrated great potential for implementing energy efficient neuro-inspired computing architectures. As a result, a wide-range of technologies have been developed that in turn are described via distinct empirical models. This diversity of technologies requires the establishment of versatile tools that can enable designers to translate memristors' attributes in novel neuro-inspired topologies. In this paper, we present NeuroPack, a modular, algorithm level Python-based simulation platform that can support studies of memristor neuro-inspired architectures for performing online learning or offline classification. The NeuroPack environment is designed with versatility being central, allowing the user to chose from a variety of neuron models, learning rules and memristors models. Its hierarchical structure, empowers NeuroPack to predict any memristor state changes and the corresponding neural network behavior across a variety of design decisions and user parameters options. The use of NeuroPack is demonstrated herein via an application example of performing handwritten digit classification with the MNIST dataset and an existing empirical model for metal-oxide memristors.