Memristor-based BAM circuit implementation for image associative memory and filling-in
Abstract Associative memory and filling-in are two essential functions of the human brain. To implement these two brain-inspired functions in hardware, we proposed a memristor-based bidirectional associative memory (BAM) circuit in this paper. This circuit combines an online algorithm with a memrist...
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Gespeichert in:
| Autor*in: |
Yang, Zijia [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Ltd., part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 33(2021), 13 vom: 08. Jan., Seite 7929-7942 |
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| Übergeordnetes Werk: |
volume:33 ; year:2021 ; number:13 ; day:08 ; month:01 ; pages:7929-7942 |
| Links: |
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| DOI / URN: |
10.1007/s00521-020-05538-7 |
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OLC2126243796 |
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| 520 | |a Abstract Associative memory and filling-in are two essential functions of the human brain. To implement these two brain-inspired functions in hardware, we proposed a memristor-based bidirectional associative memory (BAM) circuit in this paper. This circuit combines an online algorithm with a memristor array adjustment process, thus makes the circuit more universal for various tasks. The proposed circuit is constructed out of memristive synaptic circuits, IN modules and ACT modules. The memristive synaptic circuits utilize memristor arrays to represent weight matrix and operate corresponding operations hence make computing-in-memory and process information in parallel, which simplifies the complexity of circuit and improves the processing speed. The IN modules employ transistors as switches to choose the input layer hence can get initial information flow bidirectionally. The ACT modules perform activation function and can output continuous arbitrary real numbers. Thereby, both binary and gray-scale images can be tested in the proposed circuit. In addition to the hetero-association and filling-in results given in detail, the retrieval rates of the proposed circuit with the impact of different degrees of noise and the number of stored patterns are also evaluated and compared with software-based BAM. The simulation has experimented via MATLAB and PSpice, and the corresponding results show a remarkable performance of the proposed circuit. The influence of memristor’s stuck-at-fault is also considered. In comparison with software-based BAM and similar memristor-based neural network circuit, the proposed circuit performs better in processing speed. | ||
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10.1007/s00521-020-05538-7 doi (DE-627)OLC2126243796 (DE-He213)s00521-020-05538-7-p DE-627 ger DE-627 rakwb eng 004 VZ Yang, Zijia verfasserin aut Memristor-based BAM circuit implementation for image associative memory and filling-in 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Associative memory and filling-in are two essential functions of the human brain. To implement these two brain-inspired functions in hardware, we proposed a memristor-based bidirectional associative memory (BAM) circuit in this paper. This circuit combines an online algorithm with a memristor array adjustment process, thus makes the circuit more universal for various tasks. The proposed circuit is constructed out of memristive synaptic circuits, IN modules and ACT modules. The memristive synaptic circuits utilize memristor arrays to represent weight matrix and operate corresponding operations hence make computing-in-memory and process information in parallel, which simplifies the complexity of circuit and improves the processing speed. The IN modules employ transistors as switches to choose the input layer hence can get initial information flow bidirectionally. The ACT modules perform activation function and can output continuous arbitrary real numbers. Thereby, both binary and gray-scale images can be tested in the proposed circuit. In addition to the hetero-association and filling-in results given in detail, the retrieval rates of the proposed circuit with the impact of different degrees of noise and the number of stored patterns are also evaluated and compared with software-based BAM. The simulation has experimented via MATLAB and PSpice, and the corresponding results show a remarkable performance of the proposed circuit. The influence of memristor’s stuck-at-fault is also considered. In comparison with software-based BAM and similar memristor-based neural network circuit, the proposed circuit performs better in processing speed. Memristors Bidirectional associative memory Associative memory Filling-in Wang, Xiaoping (orcid)0000-0002-4909-8286 aut Enthalten in Neural computing & applications Springer London, 1993 33(2021), 13 vom: 08. Jan., Seite 7929-7942 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:33 year:2021 number:13 day:08 month:01 pages:7929-7942 https://doi.org/10.1007/s00521-020-05538-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 33 2021 13 08 01 7929-7942 |
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10.1007/s00521-020-05538-7 doi (DE-627)OLC2126243796 (DE-He213)s00521-020-05538-7-p DE-627 ger DE-627 rakwb eng 004 VZ Yang, Zijia verfasserin aut Memristor-based BAM circuit implementation for image associative memory and filling-in 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Associative memory and filling-in are two essential functions of the human brain. To implement these two brain-inspired functions in hardware, we proposed a memristor-based bidirectional associative memory (BAM) circuit in this paper. This circuit combines an online algorithm with a memristor array adjustment process, thus makes the circuit more universal for various tasks. The proposed circuit is constructed out of memristive synaptic circuits, IN modules and ACT modules. The memristive synaptic circuits utilize memristor arrays to represent weight matrix and operate corresponding operations hence make computing-in-memory and process information in parallel, which simplifies the complexity of circuit and improves the processing speed. The IN modules employ transistors as switches to choose the input layer hence can get initial information flow bidirectionally. The ACT modules perform activation function and can output continuous arbitrary real numbers. Thereby, both binary and gray-scale images can be tested in the proposed circuit. In addition to the hetero-association and filling-in results given in detail, the retrieval rates of the proposed circuit with the impact of different degrees of noise and the number of stored patterns are also evaluated and compared with software-based BAM. The simulation has experimented via MATLAB and PSpice, and the corresponding results show a remarkable performance of the proposed circuit. The influence of memristor’s stuck-at-fault is also considered. In comparison with software-based BAM and similar memristor-based neural network circuit, the proposed circuit performs better in processing speed. Memristors Bidirectional associative memory Associative memory Filling-in Wang, Xiaoping (orcid)0000-0002-4909-8286 aut Enthalten in Neural computing & applications Springer London, 1993 33(2021), 13 vom: 08. Jan., Seite 7929-7942 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:33 year:2021 number:13 day:08 month:01 pages:7929-7942 https://doi.org/10.1007/s00521-020-05538-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 33 2021 13 08 01 7929-7942 |
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10.1007/s00521-020-05538-7 doi (DE-627)OLC2126243796 (DE-He213)s00521-020-05538-7-p DE-627 ger DE-627 rakwb eng 004 VZ Yang, Zijia verfasserin aut Memristor-based BAM circuit implementation for image associative memory and filling-in 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Associative memory and filling-in are two essential functions of the human brain. To implement these two brain-inspired functions in hardware, we proposed a memristor-based bidirectional associative memory (BAM) circuit in this paper. This circuit combines an online algorithm with a memristor array adjustment process, thus makes the circuit more universal for various tasks. The proposed circuit is constructed out of memristive synaptic circuits, IN modules and ACT modules. The memristive synaptic circuits utilize memristor arrays to represent weight matrix and operate corresponding operations hence make computing-in-memory and process information in parallel, which simplifies the complexity of circuit and improves the processing speed. The IN modules employ transistors as switches to choose the input layer hence can get initial information flow bidirectionally. The ACT modules perform activation function and can output continuous arbitrary real numbers. Thereby, both binary and gray-scale images can be tested in the proposed circuit. In addition to the hetero-association and filling-in results given in detail, the retrieval rates of the proposed circuit with the impact of different degrees of noise and the number of stored patterns are also evaluated and compared with software-based BAM. The simulation has experimented via MATLAB and PSpice, and the corresponding results show a remarkable performance of the proposed circuit. The influence of memristor’s stuck-at-fault is also considered. In comparison with software-based BAM and similar memristor-based neural network circuit, the proposed circuit performs better in processing speed. Memristors Bidirectional associative memory Associative memory Filling-in Wang, Xiaoping (orcid)0000-0002-4909-8286 aut Enthalten in Neural computing & applications Springer London, 1993 33(2021), 13 vom: 08. Jan., Seite 7929-7942 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:33 year:2021 number:13 day:08 month:01 pages:7929-7942 https://doi.org/10.1007/s00521-020-05538-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 33 2021 13 08 01 7929-7942 |
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10.1007/s00521-020-05538-7 doi (DE-627)OLC2126243796 (DE-He213)s00521-020-05538-7-p DE-627 ger DE-627 rakwb eng 004 VZ Yang, Zijia verfasserin aut Memristor-based BAM circuit implementation for image associative memory and filling-in 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Associative memory and filling-in are two essential functions of the human brain. To implement these two brain-inspired functions in hardware, we proposed a memristor-based bidirectional associative memory (BAM) circuit in this paper. This circuit combines an online algorithm with a memristor array adjustment process, thus makes the circuit more universal for various tasks. The proposed circuit is constructed out of memristive synaptic circuits, IN modules and ACT modules. The memristive synaptic circuits utilize memristor arrays to represent weight matrix and operate corresponding operations hence make computing-in-memory and process information in parallel, which simplifies the complexity of circuit and improves the processing speed. The IN modules employ transistors as switches to choose the input layer hence can get initial information flow bidirectionally. The ACT modules perform activation function and can output continuous arbitrary real numbers. Thereby, both binary and gray-scale images can be tested in the proposed circuit. In addition to the hetero-association and filling-in results given in detail, the retrieval rates of the proposed circuit with the impact of different degrees of noise and the number of stored patterns are also evaluated and compared with software-based BAM. The simulation has experimented via MATLAB and PSpice, and the corresponding results show a remarkable performance of the proposed circuit. The influence of memristor’s stuck-at-fault is also considered. In comparison with software-based BAM and similar memristor-based neural network circuit, the proposed circuit performs better in processing speed. Memristors Bidirectional associative memory Associative memory Filling-in Wang, Xiaoping (orcid)0000-0002-4909-8286 aut Enthalten in Neural computing & applications Springer London, 1993 33(2021), 13 vom: 08. Jan., Seite 7929-7942 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:33 year:2021 number:13 day:08 month:01 pages:7929-7942 https://doi.org/10.1007/s00521-020-05538-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 33 2021 13 08 01 7929-7942 |
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10.1007/s00521-020-05538-7 doi (DE-627)OLC2126243796 (DE-He213)s00521-020-05538-7-p DE-627 ger DE-627 rakwb eng 004 VZ Yang, Zijia verfasserin aut Memristor-based BAM circuit implementation for image associative memory and filling-in 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Associative memory and filling-in are two essential functions of the human brain. To implement these two brain-inspired functions in hardware, we proposed a memristor-based bidirectional associative memory (BAM) circuit in this paper. This circuit combines an online algorithm with a memristor array adjustment process, thus makes the circuit more universal for various tasks. The proposed circuit is constructed out of memristive synaptic circuits, IN modules and ACT modules. The memristive synaptic circuits utilize memristor arrays to represent weight matrix and operate corresponding operations hence make computing-in-memory and process information in parallel, which simplifies the complexity of circuit and improves the processing speed. The IN modules employ transistors as switches to choose the input layer hence can get initial information flow bidirectionally. The ACT modules perform activation function and can output continuous arbitrary real numbers. Thereby, both binary and gray-scale images can be tested in the proposed circuit. In addition to the hetero-association and filling-in results given in detail, the retrieval rates of the proposed circuit with the impact of different degrees of noise and the number of stored patterns are also evaluated and compared with software-based BAM. The simulation has experimented via MATLAB and PSpice, and the corresponding results show a remarkable performance of the proposed circuit. The influence of memristor’s stuck-at-fault is also considered. In comparison with software-based BAM and similar memristor-based neural network circuit, the proposed circuit performs better in processing speed. Memristors Bidirectional associative memory Associative memory Filling-in Wang, Xiaoping (orcid)0000-0002-4909-8286 aut Enthalten in Neural computing & applications Springer London, 1993 33(2021), 13 vom: 08. Jan., Seite 7929-7942 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:33 year:2021 number:13 day:08 month:01 pages:7929-7942 https://doi.org/10.1007/s00521-020-05538-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 33 2021 13 08 01 7929-7942 |
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Memristor-based BAM circuit implementation for image associative memory and filling-in |
| abstract |
Abstract Associative memory and filling-in are two essential functions of the human brain. To implement these two brain-inspired functions in hardware, we proposed a memristor-based bidirectional associative memory (BAM) circuit in this paper. This circuit combines an online algorithm with a memristor array adjustment process, thus makes the circuit more universal for various tasks. The proposed circuit is constructed out of memristive synaptic circuits, IN modules and ACT modules. The memristive synaptic circuits utilize memristor arrays to represent weight matrix and operate corresponding operations hence make computing-in-memory and process information in parallel, which simplifies the complexity of circuit and improves the processing speed. The IN modules employ transistors as switches to choose the input layer hence can get initial information flow bidirectionally. The ACT modules perform activation function and can output continuous arbitrary real numbers. Thereby, both binary and gray-scale images can be tested in the proposed circuit. In addition to the hetero-association and filling-in results given in detail, the retrieval rates of the proposed circuit with the impact of different degrees of noise and the number of stored patterns are also evaluated and compared with software-based BAM. The simulation has experimented via MATLAB and PSpice, and the corresponding results show a remarkable performance of the proposed circuit. The influence of memristor’s stuck-at-fault is also considered. In comparison with software-based BAM and similar memristor-based neural network circuit, the proposed circuit performs better in processing speed. © Springer-Verlag London Ltd., part of Springer Nature 2021 |
| abstractGer |
Abstract Associative memory and filling-in are two essential functions of the human brain. To implement these two brain-inspired functions in hardware, we proposed a memristor-based bidirectional associative memory (BAM) circuit in this paper. This circuit combines an online algorithm with a memristor array adjustment process, thus makes the circuit more universal for various tasks. The proposed circuit is constructed out of memristive synaptic circuits, IN modules and ACT modules. The memristive synaptic circuits utilize memristor arrays to represent weight matrix and operate corresponding operations hence make computing-in-memory and process information in parallel, which simplifies the complexity of circuit and improves the processing speed. The IN modules employ transistors as switches to choose the input layer hence can get initial information flow bidirectionally. The ACT modules perform activation function and can output continuous arbitrary real numbers. Thereby, both binary and gray-scale images can be tested in the proposed circuit. In addition to the hetero-association and filling-in results given in detail, the retrieval rates of the proposed circuit with the impact of different degrees of noise and the number of stored patterns are also evaluated and compared with software-based BAM. The simulation has experimented via MATLAB and PSpice, and the corresponding results show a remarkable performance of the proposed circuit. The influence of memristor’s stuck-at-fault is also considered. In comparison with software-based BAM and similar memristor-based neural network circuit, the proposed circuit performs better in processing speed. © Springer-Verlag London Ltd., part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Associative memory and filling-in are two essential functions of the human brain. To implement these two brain-inspired functions in hardware, we proposed a memristor-based bidirectional associative memory (BAM) circuit in this paper. This circuit combines an online algorithm with a memristor array adjustment process, thus makes the circuit more universal for various tasks. The proposed circuit is constructed out of memristive synaptic circuits, IN modules and ACT modules. The memristive synaptic circuits utilize memristor arrays to represent weight matrix and operate corresponding operations hence make computing-in-memory and process information in parallel, which simplifies the complexity of circuit and improves the processing speed. The IN modules employ transistors as switches to choose the input layer hence can get initial information flow bidirectionally. The ACT modules perform activation function and can output continuous arbitrary real numbers. Thereby, both binary and gray-scale images can be tested in the proposed circuit. In addition to the hetero-association and filling-in results given in detail, the retrieval rates of the proposed circuit with the impact of different degrees of noise and the number of stored patterns are also evaluated and compared with software-based BAM. The simulation has experimented via MATLAB and PSpice, and the corresponding results show a remarkable performance of the proposed circuit. The influence of memristor’s stuck-at-fault is also considered. In comparison with software-based BAM and similar memristor-based neural network circuit, the proposed circuit performs better in processing speed. © Springer-Verlag London Ltd., part of Springer Nature 2021 |
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| container_issue |
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| title_short |
Memristor-based BAM circuit implementation for image associative memory and filling-in |
| url |
https://doi.org/10.1007/s00521-020-05538-7 |
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false |
| author2 |
Wang, Xiaoping |
| author2Str |
Wang, Xiaoping |
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| doi_str |
10.1007/s00521-020-05538-7 |
| up_date |
2024-07-04T06:24:21.496Z |
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