Edge AI chips are moving artificial intelligence out of the cloud and directly onto devices. But the real reason isn't hype — it's latency, bandwidth, privacy, power, and engineering reality.

In this episode of Silicon to Software, discover how edge AI hardware, NPUs, TinyML, and on-device AI are changing where artificial intelligence actually runs.

Cloud AI remains essential for training massive models, but real-time inference creates a different engineering problem. Autonomous vehicles, industrial vision systems, robotics, medical devices, and embedded systems often cannot afford to wait for data to travel to a remote data center and back.

At 65 mph, a vehicle travels roughly 8–19 feet during an 80–200 ms cloud round trip. In industrial automation, some machine-vision decisions need to happen  ...