FPGA vs ASIC for AI at the Edge: What factors influence your hardware choice?

Great topic. For me, the biggest deciding factor is not only raw performance, but how stable the workload is. If the model architecture, quantization format, memory access pattern, or I/O requirements are still changing, FPGA is usually the safer choice because flexibility is worth a lot at the edge. It lets the team validate the real data path, latency, thermal behavior, and deployment constraints before locking anything down.
I’ve seen FPGA-to-ASIC migration make the most sense after the model family and acceleration blocks are mature. At that point, ASIC can win strongly on power, unit cost, and predictable latency, especially for high-volume products like cameras, sensors, industrial devices, or always-on inference systems. The risk is moving too early, because every late change becomes expensive.
For evolving AI workloads, low or medium volume, custom interfaces, or products that need field updates, I would lean FPGA. For fixed workloads, tight power budgets, strict latency targets, and large production volume, ASIC becomes much more attractive. In practice, I think FPGA is often the learning and validation platform, while ASIC is the optimization step once the design has stopped movin