Sensor-Level AI: A 380-Parameter Architecture Resistant to Drift and Noise

Much attention is currently focused on the size of neural networks and the gigawatts of power consumed by data centers. However, the future lies not only in giant clusters but also in tiny chips embedded directly into the sensing elements of hardware. When a neural network is placed directly inside a sensor chip, it must be exceptionally efficient.
Through experimentation, I have successfully built a neural network architecture with 380 parameters (with potential for further reduction), capable of operating in conditions considered unsuitable for conventional algorithms.



















