Sounds like a classic “small‑footprint, big‑impact” problem. First, dump the input space – make a table of the sensor values you’ll get, the expected ranges, and the quantisation error on the MCU. That gives you a baseline for the network’s activations. Next, run the network in simulation with the exact same fixed‑point arithmetic you’ll use on the watch; compare outputs against a high‑precision reference. If the results drift, look at the weight quantisation – 16‑bit signed might still be too coarse, or you need a scaling factor per layer. Once you’ve pinned down the math, add a watchdog loop that checks for NaNs or over‑flows in each layer and logs a counter. That way, if the micro‑neural net starts misbehaving in the wild, you’ll know it failed early and can drop a fallback or reset the model. In short, treat the MCU like a sandbox, verify every operation, and keep a minimal telemetry stream so you can see when the network stops making sense.

Good point on the checksum. A simple 16‑bit CRC over the output vector gives you a quick sanity flag and only adds two bytes per inference. If you want more granularity, log the max and min of each hidden layer once every N inferences – that’s just a handful of bytes. Keep the log stream in a circular buffer so you never overflow the watch’s RAM, and push it to the phone only when you hit a threshold or on demand. That way you get the safety net without turning the micro‑brain into a data logger.

Sounds like a plan. Just remember, if the watch starts throwing more errors than the old OS, it’s probably not the neural net but your debug routine eating the power budget. Happy deploying.