Neuromorphic computing represents a fundamental departure from classical von Neumann architectures by embedding computation principles derived from biological neural systems directly into hardware. As edge artificial intelligence applications multiply across domains including autonomous vehicles, medical diagnostics, and industrial sensing, the energy constraints of conventional processors have become increasingly untenable. This paper provides a systematic descriptive analysis of neuromorphic computing architectures, characterizing their structural principles, dominant hardware implementations, spike-based computation models, and deployment contexts at the edge. Drawing on peer-reviewed literature published between 2019 and 2026, the study surveys major neuromorphic platforms, including Intel’s Loihi series, IBM’s TrueNorth, and BrainScaleS-2, alongside emerging memristive and photonic designs. Key descriptive dimensions include synaptic density, on-chip learning mechanisms, power consumption profiles, and compatibility with event-driven sensor modalities. The analysis reveals a consistent architectural orientation toward asynchronous, sparse, and local processing as the defining characteristics of neuromorphic systems suited for edge deployment. These features position neuromorphic hardware as a structurally distinct and increasingly well-documented alternative to graphics processing unit (GPU)-centric inference pipelines for latency-sensitive, power-constrained environments.