Hopping-Transport Mechanism for Reconfigurable Logic in Disordered Dopant Networks

Tertilt, Henri; Bakker, Jesse; Becker, Marlon; Wilde, Bram de; Klanberg, Indrek; Geurts, Bernardus J.; Wiel, Wilfred G. van der; Heuer, Andreas; Bobbert, Peter A.

doi:10.1103/physrevapplied.17.064025

Cited by 6 publications

(1 citation statement)

References 21 publications

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“…In another context, when developing nanoelectronic devices for cryptographic functionality [22], it is desirable that there exists no compact models. Further experiments or simulations (such as Monte Carlo [23], [24] and mean-field [3, Section 2.2.2] methods) are warranted to find and exploit the compactness (or lack thereof) of functionality in disordered nanoclusters made out of different recipes of materials, device structures, and manufacturing processes.…”

Section: Novelty Of the Main Resultsmentioning

confidence: 99%

Compact Modeling of Nanocluster Functionality as a Higher-Order Neuron

Lawrence

2022

IEEE Trans. Electron Devices

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Disordered nanoclusters with multi-electrode inputoutput functionality had recently been experimentally realized with energy-efficient and emergent computational capacity, and thus an interconnected network of several such nanoclusters had been proposed to realize artificial neural networks. To aid that end, here we show that nanocluster functionality can be fit to the simplest dendritic neuron model, where the only form of nonlinearity is due to multiplicative interactions. This work brings in to the spotlight higher-order neural networks (known for their efficient encoding of geometric invariances) to serve as an explainable baseline model of nano-networks against which experimentalists can compare more sophisticated models (deep neural networks or physics-based models such as the lin-min network introduced here); and provides ground for designing novel approximate hardware and a statistical mechanics analysis of the learning performance of interconnected nanoclusters vs. perceptrons (where neurons output a nonlinear function of the weighted sum of their inputs). A network with just 10 higherorder neurons is shown to achieve a classification accuracy of more than 96% on the MNIST benchmark for handwritten digit recognition (which required 100 times more neurons in 3-layer perceptrons).

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Section: Novelty Of the Main Resultsmentioning

confidence: 99%

Compact Modeling of Nanocluster Functionality as a Higher-Order Neuron

Lawrence

2022

IEEE Trans. Electron Devices

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Critical nonlinear aspects of hopping transport for reconfigurable logic in disordered dopant networks

Tertilt,

Mensing,

Becker

et al. 2024

Phys. Rev. Applied

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Brain-inspired computing systems: a systematic literature review

Zolfagharinejad,

Alegre-Ibarra,

Chen

et al. 2024

Eur. Phys. J. B

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Brain-inspired computing is a growing and interdisciplinary area of research that investigates how the computational principles of the biological brain can be translated into hardware design to achieve improved energy efficiency. Brain-inspired computing encompasses various subfields, including neuromorphic and in-memory computing, that have been shown to outperform traditional digital hardware in executing specific tasks. With the rising demand for more powerful yet energy-efficient hardware for large-scale artificial neural networks, brain-inspired computing is emerging as a promising solution for enabling energy-efficient computing and expanding AI to the edge. However, the vast scope of the field has made it challenging to compare and assess the effectiveness of the solutions compared to state-of-the-art digital counterparts. This systematic literature review provides a comprehensive overview of the latest advances in brain-inspired computing hardware. To ensure accessibility for researchers from diverse backgrounds, we begin by introducing key concepts and pointing out respective in-depth topical reviews. We continue with categorizing the dominant hardware platforms. We highlight various studies and potential applications that could greatly benefit from brain-inspired computing systems and compare their reported computational accuracy. Finally, to have a fair comparison of the performance of different approaches, we employ a standardized normalization approach for energy efficiency reports in the literature. Graphical abstract

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Hopping-Transport Mechanism for Reconfigurable Logic in Disordered Dopant Networks

Cited by 6 publications

References 21 publications

Compact Modeling of Nanocluster Functionality as a Higher-Order Neuron

Compact Modeling of Nanocluster Functionality as a Higher-Order Neuron

Critical nonlinear aspects of hopping transport for reconfigurable logic in disordered dopant networks

Brain-inspired computing systems: a systematic literature review

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