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., 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. Neuromorphic computing promises energy-efficient AI at the edge by mimicking biological brains. Sayeed Chowdhury and colleagues review recent progress in sensing, algorithms, and hardware, and outline future research directions in this domain.