Nhdta-793

On‑device inference and learning diminish the need to stream raw sensor data to centralized servers, mitigating privacy risks. However, the capacity for continuous adaptation also raises concerns about —users may be unaware of how a device’s behavior has evolved over time.

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Smart grids, industrial IoT, and predictive maintenance rely on streaming sensor data that exhibits non‑stationary statistics. The processor’s built‑in plasticity enables , allowing infrastructure nodes to adapt to evolving load patterns or equipment wear without costly firmware updates. On‑device inference and learning diminish the need to

max_len_inside = 12 # try up to 12 characters inside the braces mitigating privacy risks. However