Evaluation of fluxon synapse device based on superconducting loops for energy efficient neuromorphic computing
With Moore’s law nearing its end due to the physical scaling limitations of CMOS technology, alternative computing approaches have gained considerable attention as ways to improve computing performance. Here, we evaluate performance prospects of a new approach based on disordered superconducting loo...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1511371/full |
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| author | Ashwani Kumar Uday S. Goteti Ertugrul Cubukcu Robert C. Dynes Duygu Kuzum |
| author_facet | Ashwani Kumar Uday S. Goteti Ertugrul Cubukcu Robert C. Dynes Duygu Kuzum |
| author_sort | Ashwani Kumar |
| collection | DOAJ |
| description | With Moore’s law nearing its end due to the physical scaling limitations of CMOS technology, alternative computing approaches have gained considerable attention as ways to improve computing performance. Here, we evaluate performance prospects of a new approach based on disordered superconducting loops with Josephson-junctions for energy efficient neuromorphic computing. Synaptic weights can be stored as internal trapped fluxon states of three superconducting loops connected with multiple Josephson-junctions (JJ) and modulated by input signals applied in the form of discrete fluxons (quantized flux) in a controlled manner. The stable trapped fluxon state directs the incoming flux through different pathways with the flow statistics representing different synaptic weights. We explore implementation of matrix–vector-multiplication (MVM) operations using arrays of these fluxon synapse devices. We investigate the energy efficiency of online-learning of MNIST dataset. Our results suggest that the fluxon synapse array can provide ~100× reduction in energy consumption compared to other state-of-the-art synaptic devices. This work presents a proof-of-concept that will pave the way for development of high-speed and highly energy efficient neuromorphic computing systems based on superconducting materials. |
| format | Article |
| id | doaj-art-e548d7a4ed574beba1a328433e99a9ac |
| institution | DOAJ |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-e548d7a4ed574beba1a328433e99a9ac2025-08-20T03:12:39ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-02-011910.3389/fnins.2025.15113711511371Evaluation of fluxon synapse device based on superconducting loops for energy efficient neuromorphic computingAshwani Kumar0Uday S. Goteti1Ertugrul Cubukcu2Robert C. Dynes3Duygu Kuzum4Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA, United StatesDepartment of Physics, University of California San Diego, San Diego, CA, United StatesDepartment of Chemical and Nano Engineering, University of California San Diego, San Diego, CA, United StatesDepartment of Physics, University of California San Diego, San Diego, CA, United StatesDepartment of Electrical and Computer Engineering, University of California San Diego, San Diego, CA, United StatesWith Moore’s law nearing its end due to the physical scaling limitations of CMOS technology, alternative computing approaches have gained considerable attention as ways to improve computing performance. Here, we evaluate performance prospects of a new approach based on disordered superconducting loops with Josephson-junctions for energy efficient neuromorphic computing. Synaptic weights can be stored as internal trapped fluxon states of three superconducting loops connected with multiple Josephson-junctions (JJ) and modulated by input signals applied in the form of discrete fluxons (quantized flux) in a controlled manner. The stable trapped fluxon state directs the incoming flux through different pathways with the flow statistics representing different synaptic weights. We explore implementation of matrix–vector-multiplication (MVM) operations using arrays of these fluxon synapse devices. We investigate the energy efficiency of online-learning of MNIST dataset. Our results suggest that the fluxon synapse array can provide ~100× reduction in energy consumption compared to other state-of-the-art synaptic devices. This work presents a proof-of-concept that will pave the way for development of high-speed and highly energy efficient neuromorphic computing systems based on superconducting materials.https://www.frontiersin.org/articles/10.3389/fnins.2025.1511371/fullneuromorphic computingsuperconducting loopsJosephson junctionsdeep learningimage classificationenergy efficient hardware |
| spellingShingle | Ashwani Kumar Uday S. Goteti Ertugrul Cubukcu Robert C. Dynes Duygu Kuzum Evaluation of fluxon synapse device based on superconducting loops for energy efficient neuromorphic computing Frontiers in Neuroscience neuromorphic computing superconducting loops Josephson junctions deep learning image classification energy efficient hardware |
| title | Evaluation of fluxon synapse device based on superconducting loops for energy efficient neuromorphic computing |
| title_full | Evaluation of fluxon synapse device based on superconducting loops for energy efficient neuromorphic computing |
| title_fullStr | Evaluation of fluxon synapse device based on superconducting loops for energy efficient neuromorphic computing |
| title_full_unstemmed | Evaluation of fluxon synapse device based on superconducting loops for energy efficient neuromorphic computing |
| title_short | Evaluation of fluxon synapse device based on superconducting loops for energy efficient neuromorphic computing |
| title_sort | evaluation of fluxon synapse device based on superconducting loops for energy efficient neuromorphic computing |
| topic | neuromorphic computing superconducting loops Josephson junctions deep learning image classification energy efficient hardware |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1511371/full |
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