Ipvr-264 May 2026

Recent research has explored adaptive regulators that modulate architecture or control parameters in response to workload, yet most solutions rely on pre‑programmed heuristics, limiting their ability to cope with highly stochastic traffic patterns typical of edge‑node radios (e.g., BLE advertising, LoRaWAN Class A uplinks) [4]. Moreover, the lack of a unified approach that simultaneously addresses mode transition losses , dynamic load prediction , and switching‑frequency optimization leaves a substantial gap in achieving true ultra‑low‑power operation.

[ \hatI load[k+1] = \sigma!\Big(\sum i=0^7 w_i \cdot I_load[k-i] + b\Big) ] IPVR-264

where σ is the ReLU function. Offline training minimizes mean‑square error (MSE) over a Offline training minimizes mean‑square error (MSE) over a

| Unit | Function | Key Parameters | |------|----------|----------------| | PLE | Predict next‑cycle load current based on recent activity (last 8 samples) using a two‑layer perceptron (8 × 4 × 1) with ReLU activation. | 32 bytes SRAM, 0.8 µW power | | DFS | Adjust the switching frequency f_sw between 0.5 MHz and 5 MHz to maintain a target inductor current ripple (I_ripple = 15 % of I_load). | Frequency step 0.5 MHz | | MDL | Decide buck or boost mode, and set the PWM duty ratio D = Vout/Vin (buck) or D = Vin/(Vout + V_f) (boost). | Hysteresis 50 mV | | Hysteresis 50 mV |