Neuromorphic computing for robotic vision: algorithms to hardware advances
Abstract Neuromorphic computing offers transformative potential for AI in resource-constrained environments by mimicking biological neural efficiency. This perspective article analyzes recent advances and future directions, advocating a system design approach that integrates specialized sensing (e.g...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Communications Engineering |
| Online Access: | https://doi.org/10.1038/s44172-025-00492-5 |
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| _version_ | 1849333507768188928 |
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| author | Sayeed Shafayet Chowdhury Deepika Sharma Adarsh Kosta Kaushik Roy |
| author_facet | Sayeed Shafayet Chowdhury Deepika Sharma Adarsh Kosta Kaushik Roy |
| author_sort | Sayeed Shafayet Chowdhury |
| collection | DOAJ |
| description | Abstract Neuromorphic computing offers transformative potential for AI in resource-constrained environments by mimicking biological neural efficiency. This perspective article analyzes recent advances and future directions, advocating a system design approach that integrates specialized sensing (e.g., event-based cameras), brain-inspired algorithms (SNNs and SNN-ANN hybrids), and dedicated neuromorphic hardware. Using vision-based drone navigation (VDN) as an exemplar—drawing parallels with biological systems like Drosophila—we demonstrate how these components enable event-driven processing and overcome von Neumann architecture limitations through near-/in-memory computing. Key challenges include large-scale integration, benchmarking standardization, and algorithm-hardware co-design for emerging applications, which we discuss alongside current and future research directions. |
| format | Article |
| id | doaj-art-b1e5e2d55dea40c09a98130cd159f575 |
| institution | Kabale University |
| issn | 2731-3395 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Engineering |
| spelling | doaj-art-b1e5e2d55dea40c09a98130cd159f5752025-08-20T03:45:49ZengNature PortfolioCommunications Engineering2731-33952025-08-014111410.1038/s44172-025-00492-5Neuromorphic computing for robotic vision: algorithms to hardware advancesSayeed Shafayet Chowdhury0Deepika Sharma1Adarsh Kosta2Kaushik Roy3Elmore Family School of Electrical and Computer Engineering, Purdue UniversityElmore Family School of Electrical and Computer Engineering, Purdue UniversityElmore Family School of Electrical and Computer Engineering, Purdue UniversityElmore Family School of Electrical and Computer Engineering, Purdue UniversityAbstract Neuromorphic computing offers transformative potential for AI in resource-constrained environments by mimicking biological neural efficiency. This perspective article analyzes recent advances and future directions, advocating a system design approach that integrates specialized sensing (e.g., event-based cameras), brain-inspired algorithms (SNNs and SNN-ANN hybrids), and dedicated neuromorphic hardware. Using vision-based drone navigation (VDN) as an exemplar—drawing parallels with biological systems like Drosophila—we demonstrate how these components enable event-driven processing and overcome von Neumann architecture limitations through near-/in-memory computing. Key challenges include large-scale integration, benchmarking standardization, and algorithm-hardware co-design for emerging applications, which we discuss alongside current and future research directions.https://doi.org/10.1038/s44172-025-00492-5 |
| spellingShingle | Sayeed Shafayet Chowdhury Deepika Sharma Adarsh Kosta Kaushik Roy Neuromorphic computing for robotic vision: algorithms to hardware advances Communications Engineering |
| title | Neuromorphic computing for robotic vision: algorithms to hardware advances |
| title_full | Neuromorphic computing for robotic vision: algorithms to hardware advances |
| title_fullStr | Neuromorphic computing for robotic vision: algorithms to hardware advances |
| title_full_unstemmed | Neuromorphic computing for robotic vision: algorithms to hardware advances |
| title_short | Neuromorphic computing for robotic vision: algorithms to hardware advances |
| title_sort | neuromorphic computing for robotic vision algorithms to hardware advances |
| url | https://doi.org/10.1038/s44172-025-00492-5 |
| work_keys_str_mv | AT sayeedshafayetchowdhury neuromorphiccomputingforroboticvisionalgorithmstohardwareadvances AT deepikasharma neuromorphiccomputingforroboticvisionalgorithmstohardwareadvances AT adarshkosta neuromorphiccomputingforroboticvisionalgorithmstohardwareadvances AT kaushikroy neuromorphiccomputingforroboticvisionalgorithmstohardwareadvances |