Edge Intelligence for Smart Cities: FPGA and TinyML Infrastructure
FPGA TinyML smart cities shift urban digital infrastructure from cloud-dependent data collection toward distributed edge intelligence. By combining embedded systems, TinyML, and FPGA acceleration, cities can process signals locally where latency, energy efficiency, privacy, and operational continuity matter most. This article examines how on-device inference and configurable hardware can support traffic systems, water monitoring, environmental sensors, transit infrastructure, grid diagnostics, and adaptive public services without transmitting every signal to centralized platforms. It argues that edge intelligence is not merely a performance upgrade; it is a resilience architecture. For smart-city systems to remain trustworthy, they must also be secure, auditable, version-controlled, maintainable, and governed across the full lifecycle of models, firmware, FPGA configurations, sensors, and public infrastructure decisions.