Complex chemical reaction networks for future information processing
Tackling the increasing energy demand of our society is one of the key challenges today. With the rise of artificial intelligence, information and communication technologies started to substantially contribute to this alarming trend and therefore necessitate more sustainable approaches for the futur...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1379205/full |
| _version_ | 1797263560245510144 |
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| author | Katja-Sophia Csizi Emanuel Lörtscher |
| author_facet | Katja-Sophia Csizi Emanuel Lörtscher |
| author_sort | Katja-Sophia Csizi |
| collection | DOAJ |
| description | Tackling the increasing energy demand of our society is one of the key challenges today. With the rise of artificial intelligence, information and communication technologies started to substantially contribute to this alarming trend and therefore necessitate more sustainable approaches for the future. Brain-inspired computing paradigms represent a radically new and potentially more energy-efficient approach for computing that may complement or even replace CMOS in the long term. In this perspective, we elaborate on the concepts and properties of complex chemical reaction networks (CRNs) that may serve as information-processing units based on chemical reactions. The computational capabilities of simpler, oscillatory chemical reactions have already been demonstrated in scenarios ranging from the emulation of Boolean gates to image-processing tasks. CRNs offer higher complexity and larger non-linearity, potentially at lower energy consumption. Key challenges for the successful development of CRN-based computers are associated with their specific physical implementations, operability, and readout modalities. CRNs are sensible to various reaction triggers, and provide multiple and interlinked reaction pathways and a diverse compound space. This bears a high potential to build radically new hardware and software concepts for energy-efficient computing based on neuromorphic architectures—with computing capabilities in real-world applications yet to be demonstrated. |
| first_indexed | 2024-04-25T00:14:57Z |
| format | Article |
| id | doaj.art-d0fb1415cb7d4fc5a38a1bcbcec703c7 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-25T00:14:57Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-d0fb1415cb7d4fc5a38a1bcbcec703c72024-03-13T04:58:11ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2024-03-011810.3389/fnins.2024.13792051379205Complex chemical reaction networks for future information processingKatja-Sophia CsiziEmanuel LörtscherTackling the increasing energy demand of our society is one of the key challenges today. With the rise of artificial intelligence, information and communication technologies started to substantially contribute to this alarming trend and therefore necessitate more sustainable approaches for the future. Brain-inspired computing paradigms represent a radically new and potentially more energy-efficient approach for computing that may complement or even replace CMOS in the long term. In this perspective, we elaborate on the concepts and properties of complex chemical reaction networks (CRNs) that may serve as information-processing units based on chemical reactions. The computational capabilities of simpler, oscillatory chemical reactions have already been demonstrated in scenarios ranging from the emulation of Boolean gates to image-processing tasks. CRNs offer higher complexity and larger non-linearity, potentially at lower energy consumption. Key challenges for the successful development of CRN-based computers are associated with their specific physical implementations, operability, and readout modalities. CRNs are sensible to various reaction triggers, and provide multiple and interlinked reaction pathways and a diverse compound space. This bears a high potential to build radically new hardware and software concepts for energy-efficient computing based on neuromorphic architectures—with computing capabilities in real-world applications yet to be demonstrated.https://www.frontiersin.org/articles/10.3389/fnins.2024.1379205/fullchemical computingneuromorphic computingchemical reaction networkslow-energybrain-inspired |
| spellingShingle | Katja-Sophia Csizi Emanuel Lörtscher Complex chemical reaction networks for future information processing Frontiers in Neuroscience chemical computing neuromorphic computing chemical reaction networks low-energy brain-inspired |
| title | Complex chemical reaction networks for future information processing |
| title_full | Complex chemical reaction networks for future information processing |
| title_fullStr | Complex chemical reaction networks for future information processing |
| title_full_unstemmed | Complex chemical reaction networks for future information processing |
| title_short | Complex chemical reaction networks for future information processing |
| title_sort | complex chemical reaction networks for future information processing |
| topic | chemical computing neuromorphic computing chemical reaction networks low-energy brain-inspired |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2024.1379205/full |
| work_keys_str_mv | AT katjasophiacsizi complexchemicalreactionnetworksforfutureinformationprocessing AT emanuellortscher complexchemicalreactionnetworksforfutureinformationprocessing |