NeuroPack: an Algorithm-Level Python-Based Simulator for Memristor-Empowered Neuro-Inspired Computing
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 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.
memristor, neural networks, neuro-inspired computing, neuromorphic computing, offline classification, online learning
Huang, Jinqi
de5be89e-1542-4658-9d67-6757f7075c01
Stathopoulos, Spyros
98d12f06-ad01-4708-be19-a97282968ee6
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Prodromakis, Themis
d58c9c10-9d25-4d22-b155-06c8437acfbf
20 April 2022
Huang, Jinqi
de5be89e-1542-4658-9d67-6757f7075c01
Stathopoulos, Spyros
98d12f06-ad01-4708-be19-a97282968ee6
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Prodromakis, Themis
d58c9c10-9d25-4d22-b155-06c8437acfbf
Huang, Jinqi, Stathopoulos, Spyros, Serb, Alexantrou and Prodromakis, Themis
(2022)
NeuroPack: an Algorithm-Level Python-Based Simulator for Memristor-Empowered Neuro-Inspired Computing.
Frontiers in Nanotechnology, 4, [851856].
(doi:10.3389/fnano.2022.851856).
Abstract
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.
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fnano-04-851856
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More information
Accepted/In Press date: 3 March 2022
Published date: 20 April 2022
Additional Information:
Funding Information:
The authors acknowledge the support of the EPSRC FORTE Programme Grant (EP/R024642/1) and the RAEng Chair in Emerging Technologies (CiET 1819/2/93), as well as the EU projects SYNCH (824162) and CHIST-ERA net SMALL.
Keywords:
memristor, neural networks, neuro-inspired computing, neuromorphic computing, offline classification, online learning
Identifiers
Local EPrints ID: 472800
URI: http://eprints.soton.ac.uk/id/eprint/472800
PURE UUID: 1dbaffaf-53d3-4e2e-a0da-bf3a2d2e5ecc
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Date deposited: 19 Dec 2022 17:42
Last modified: 17 Mar 2024 13:12
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Contributors
Author:
Jinqi Huang
Author:
Spyros Stathopoulos
Author:
Alexantrou Serb
Author:
Themis Prodromakis
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