Integer Factorization with Stochastic Spiking in Percolating Networks of Nanoparticles

As growth in global demand for computing power continues to outpace ongoing improvements in transistor-based hardware, novel computing solutions are required. One promising approach employs stochastic nanoscale devices to accelerate probabilistic computing algorithms. Percolating Networks of Nanoparticles (PNNs) exhibit stochastic spiking, which is of particular interest as it meets criteria for criticality which is associated with a range of computational advantages. Here, we show several ways in which spiking PNNs can be used as the core stochastic components of coupled networks that allow successful factorization of integers up to 945. We demonstrate asynchronous operation and show that a single device is sufficient to solve all factorization tasks and to generate multiple solutions simultaneously.

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Publication Details

Journal
ACS Nano
Published
2024-10-03
DOI
https://doi.org/10.1021/acsnano.4c07200
Citations
5
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.04

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article

Integer Factorization with Stochastic Spiking in Percolating Networks of Nanoparticles

Sofie J. Studholme, S. A. Brown
5 citations
ACS Nano
Advanced Memory and Neural Computing
1.04
article

Integer Factorization with Stochastic Spiking in Percolating Networks of Nanoparticles

Sofie J. Studholme, S. A. Brown
article en
5 citations

Abstract

As growth in global demand for computing power continues to outpace ongoing improvements in transistor-based hardware, novel computing solutions are required. One promising approach employs stochastic nanoscale devices to accelerate probabilistic computing algorithms. Percolating Networks of Nanoparticles (PNNs) exhibit stochastic spiking, which is of particular interest as it meets criteria for criticality which is associated with a range of computational advantages. Here, we show several ways in which spiking PNNs can be used as the core stochastic components of coupled networks that allow successful factorization of integers up to 945. We demonstrate asynchronous operation and show that a single device is sufficient to solve all factorization tasks and to generate multiple solutions simultaneously.

ACS NanoVol. 18(41)
University of Canterbury (NZ), MacDiarmid Institute for Advanced Materials and Nanotechnology (NZ)
MacDiarmid Institute for Advanced Materials and Nanotechnology, Marsden Fund
Openalex Percentile: Top 23%
Advanced Memory and Neural Computing
1.04
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Integer Factorization with Stochastic Spiking in Percolating Networks of Nanoparticles — Sofie J. Studholme, S. A. Brown · ACS Nano (2024) | TGRS Research Map | TGRS