G. Martini, E. Tentori, M. Mirigliano, D. E. Galli, P. Milani, F. Mambretti
{"title":"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","doi":"10.3389/fphy.2024.1400919","DOIUrl":null,"url":null,"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 <jats:italic>n</jats:italic>-inputs devices has been proposed, where the associated weights depend on all the input values. This model, called <jats:italic>receptron</jats:italic>, 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.","PeriodicalId":12507,"journal":{"name":"Frontiers in Physics","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Physics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.3389/fphy.2024.1400919","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Frontiers in Physics publishes rigorously peer-reviewed research across the entire field, from experimental, to computational and theoretical physics. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, engineers and the public worldwide.