Fig. 1: A comprehensive neuromorphic computing pipeline for vision-based drone navigation (VDN), spanning from sensing to learning to hardware implementation.

(Top-left) Inputs and Sensing: Depiction of frame- and event-based inputs for multiple moving objects, demonstrating how traditional frame cameras capture analog intensity information while event cameras detect motion-induced intensity variations. The biological pathway shows the human eye, illustrating how biological systems perceive changes in intensity and color. The neuromorphic sensing section displays both frame-based cameras and event-based cameras working together to capture similar information. (Top-right) Neural Processing: The top biological networks section depicts neurons with synapses and interconnected neural pathways, representing the brain’s highly parallel and recurrent connections that perform computations within memory itself for exceptional efficiency. The central neuromorphic algorithms section shows various neuron types including ReLU and LIF (Leaky Integrate-and-Fire) neurons, alongside different network architectures: ANNs (Artificial Neural Networks), SNNs (Spiking Neural Networks), and Hybrid SNN-ANN configurations that combine the best of both approaches for optimal algorithmic accuracy and efficiency. The hardware section presents conventional processors (CPU, GPU, FPGA) alongside neuromorphic-specific architectures including NVM-based IMC (Non-Volatile Memory-based In-Memory Computing) and hybrid hardware solutions, utilizing device technologies such as RRAM and STT-MRAM that can efficiently implement synaptic memories and work with CPU/GPU architectures for improved efficiency and latency. (bottom) Practical implementation for vision-based drone navigation (VDN), showcasing four key vision tasks: optical flow, depth estimation, segmentation, and object detection.