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Efficiency and controllability of stochastic boolean function generation by a random network of non-linear nanoparticle junctions

Authors :
G. Martini
E. Tentori
M. Mirigliano
D. E. Galli
P. Milani
F. Mambretti
Source :
Frontiers in Physics, Vol 12 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

Abstract

Amid efforts to address energy consumption in modern computing systems, one promising approach takes advantage of random networks of non-linear nanoscale junctions formed by nanoparticles as substrates for neuromorphic computing. These networks exhibit emergent complexity and collective behaviors akin to biological neural networks, characterized by self-organization, redundancy, and non-linearity. Based on this foundation, a generalization of n-inputs devices has been proposed, where the associated weights depend on all the input values. This model, called receptron, has demonstrated its capability to generate Boolean functions as output, representing a significant breakthrough in unconventional computing methods. In this work, we characterize and present two actual implementations of this paradigm. One approach leverages the nanoscale properties of cluster-assembled Au films, while the other utilizes the recently introduced Stochastic Resistor Network (SRN) model. We first provide a concise overview of the electrical properties of these systems, emphasizing the insights gained from the SRN regarding the physical processes within real nanostructured gold films at a coarse-grained scale. Furthermore, we present evidence indicating the minimum complexity level required by the SRN model to achieve a stochastic dynamics adequate to effectively model a novel component for logic systems. To support our argument that these systems are preferable to conventional random search algorithms, we discuss quantitative criteria based on Information-theoretic tools. This suggests a practical means to steer the stochastic dynamics of the system in a controlled way, thus focusing its random exploration where it is most useful.

Details

Language :
English
ISSN :
2296424X
Volume :
12
Database :
Directory of Open Access Journals
Journal :
Frontiers in Physics
Publication Type :
Academic Journal
Accession number :
edsdoj.7ed373a4a5aa48708c2699764f8cc87f
Document Type :
article
Full Text :
https://doi.org/10.3389/fphy.2024.1400919