Computer vision model shrinks 125x — now runs fire detection on a single chip in real time

Researchers compressed an AI model for detecting jet fires from 7.5 million parameters down to 59,000 — small enough to run on a single embedded chip with no cloud connection. This means industrial facilities can now monitor flame behavior in real time, locally, with minimal latency and no dependency on external servers.

Fire detection in industrial settings has relied on either human monitoring or cloud-based video analysis, both slow and unreliable. Shrinking the model this dramatically changes the economics: a single chip running locally can now detect abnormal flame behavior faster than either alternative, and it works even if your internet connection fails. The real gain is latency — from seconds (cloud round-trip) to 30 frames per second (on-device). That difference matters when a jet flame is seconds away from becoming a catastrophe.