Abstract
Major efforts to reproduce the brain performances in terms of classification and pattern recognition have been focussed on the development of artificial neuromorphic systems based on top-down lithographic technologies typical of highly integrated components of digital computers. Unconventional computing has been proposed as an alternative exploiting the complexity and collective phenomena originating from various classes of physical substrates. Materials composed of a large number of non-linear nanoscale junctions are of particular interest: these systems, obtained by the self-assembling of nano-objects like nanoparticles and nanowires, results in non-linear conduction properties characterized by spatiotemporal correlation in their electrical activity. This appears particularly useful for classification of complex features: nonlinear projection into a high-dimensional space can make data linearly separable, providing classification solutions that are computationally very expensive with digital computers. Recently we reported that nanostructured Au films fabricated from the assembling of gold clusters by supersonic cluster beam deposition show a complex resistive switching behaviour. Their non-linear electric behaviour is remarkably stable and reproducible allowing the facile training of the devices on precise resistive states. Here we report about the fabrication and characterization of a device that allows the binary classification of Boolean functions by exploiting the properties of cluster-assembled Au films interconnecting a generic pattern of electrodes. This device, that constitutes a generalization of the perceptron, can receive inputs from different electrode configurations and generate a complete set of Boolean functions of n variables for classification tasks. We also show that the non-linear and non-local electrical conduction of cluster-assembled gold films, working at room temperature, allows the classification of non-linearly separable functions without previous training of the device.
Highlights
Human brain can efficiently retrieve information from limited or noisy inputs with extremely cri pt low power consumption and significantly outperform digital computers in tasks such as image recognition, linguistic comprehension, abstract reasoning, classification [1,2,3]
The performances of digital computers are affected by the time and energy spent moving data between the memory and the processor units [4,5,6], whereas brain architecture is based on massive parallelism, combination in the same hardware of data processing and memory storage functions, self-learning, and adaptive capabilities [2]
Major efforts to reproduce the brain performances in terms of classification and pattern recognition have been focused on the development an of the so-called “neuromorphic systems” in order to overcome the limitations of von Neumann architecture and to match the efficiency of biological systems [9,10,11,12,13,14]
Summary
Dense Network of Metallic Nanojunctions cri pt Matteo Mirigliano, Bruno Paroli, Gianluca Martini, Marco Fedrizzi, Andrea Falqui, Alberto Casu, Paolo Milani. Materials composed of a large number of non-linear nanoscale junctions are of particular interest: these systems, obtained by the self-assembling of nano-objects like nanoparticles and nanowires, results in non-linear conduction properties characterized by spatiotemporal correlation in their electrical activity. This appears useful for classification of complex features: nonlinear projection into a high-dimensional space can make data linearly separable, providing classification solutions that are computationally very expensive with digital computers. We reported that nanostructured Au films fabricated from the assembling of gold clusters by supersonic cluster beam deposition show a complex resistive switching behaviour Their non-linear electric behaviour is remarkably stable and reproducible allowing the facile training of the devices on precise resistive states.
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