Edge AI system developers often assume that AI workloads require a GPU or NPU. But when cost, latency, complex I/O or tight power budgets dominate, FPGAs offer compelling advantages. In this talk we’ll explore how FPGAs serve not just as a compute block, but as a system integration and acceleration platform that can combine tailored sensor I/O, signal processing, pre/post-processing and neural inference on one device. We’ll also show how to map AI models onto FPGAs without doing custom hardware design, using two practical on-ramps: (1) a software-first flow that generates custom instructions callable from C, and (2) a turnkey CNN acceleration block. Using representative embedded vision workloads, we’ll show apples-to-apples benchmarks. Attendees will leave with a decision checklist and concrete “first experiment” plan.