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 |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2731-3395 |