NeuroPack: An Algorithm-Level Python-Based Simulator for Memristor-Empowered Neuro-Inspired Computing
Emerging two-terminal nanoscale memory devices, known as memristors, have demonstrated great potential for implementing energy-efficient neuro-inspired computing architectures over the past decade. As a result, a wide range of technologies have been developed that, in turn, are described via distinc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Nanotechnology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnano.2022.851856/full |
| _version_ | 1818188731777024000 |
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| author | Jinqi Huang Spyros Stathopoulos Alexantrou Serb Themis Prodromakis |
| author_facet | Jinqi Huang Spyros Stathopoulos Alexantrou Serb Themis Prodromakis |
| author_sort | Jinqi Huang |
| collection | DOAJ |
| description | Emerging two-terminal nanoscale memory devices, known as memristors, have demonstrated great potential for implementing energy-efficient neuro-inspired computing architectures over the past decade. 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 study, 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 choose from a variety of neuron models, learning rules, and memristor 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 parameter 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. |
| first_indexed | 2024-12-11T23:31:35Z |
| format | Article |
| id | doaj.art-8d136087f9f0426c84dd2eda3bc3a049 |
| institution | Directory Open Access Journal |
| issn | 2673-3013 |
| language | English |
| last_indexed | 2024-12-11T23:31:35Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Nanotechnology |
| spelling | doaj.art-8d136087f9f0426c84dd2eda3bc3a0492022-12-22T00:46:02ZengFrontiers Media S.A.Frontiers in Nanotechnology2673-30132022-04-01410.3389/fnano.2022.851856851856NeuroPack: An Algorithm-Level Python-Based Simulator for Memristor-Empowered Neuro-Inspired ComputingJinqi HuangSpyros StathopoulosAlexantrou SerbThemis ProdromakisEmerging two-terminal nanoscale memory devices, known as memristors, have demonstrated great potential for implementing energy-efficient neuro-inspired computing architectures over the past decade. 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 study, 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 choose from a variety of neuron models, learning rules, and memristor 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 parameter 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.https://www.frontiersin.org/articles/10.3389/fnano.2022.851856/fullmemristorneuro-inspired computingneuromorphic computingneural networksonline learningoffline classification |
| spellingShingle | Jinqi Huang Spyros Stathopoulos Alexantrou Serb Themis Prodromakis NeuroPack: An Algorithm-Level Python-Based Simulator for Memristor-Empowered Neuro-Inspired Computing Frontiers in Nanotechnology memristor neuro-inspired computing neuromorphic computing neural networks online learning offline classification |
| title | NeuroPack: An Algorithm-Level Python-Based Simulator for Memristor-Empowered Neuro-Inspired Computing |
| title_full | NeuroPack: An Algorithm-Level Python-Based Simulator for Memristor-Empowered Neuro-Inspired Computing |
| title_fullStr | NeuroPack: An Algorithm-Level Python-Based Simulator for Memristor-Empowered Neuro-Inspired Computing |
| title_full_unstemmed | NeuroPack: An Algorithm-Level Python-Based Simulator for Memristor-Empowered Neuro-Inspired Computing |
| title_short | NeuroPack: An Algorithm-Level Python-Based Simulator for Memristor-Empowered Neuro-Inspired Computing |
| title_sort | neuropack an algorithm level python based simulator for memristor empowered neuro inspired computing |
| topic | memristor neuro-inspired computing neuromorphic computing neural networks online learning offline classification |
| url | https://www.frontiersin.org/articles/10.3389/fnano.2022.851856/full |
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