Fix battery level threshold notation in power optimization docs

docs/operations/optimize-edge-ai-power-performance.md

@@ -45,7 +45,7 @@ Implement power-aware AI inference strategies for edge devices that dynamically
 
 - **Defer non-urgent inference**: Queue and batch low-priority inference requests to run during optimal power conditions (e.g., when device is charging or plugged in).
 - **Thermal-aware throttling**: Monitor device temperature and reduce inference frequency or model complexity before hardware throttling occurs.
-- **Battery-level policies**: Implement tiered power policies that progressively reduce AI workload intensity as battery depletes (e.g., 100-70%: full performance, 70-30%: reduced frequency, <30%: minimal/disabled).
+- **Battery-level policies**: Implement tiered power policies that progressively reduce AI workload intensity as battery depletes (e.g., 100-70%: full performance, 70-30%: reduced frequency, below 30%: minimal/disabled).
 - **Enable dynamic voltage and frequency scaling (DVFS)**: Configure hardware to operate at lower frequencies when peak performance isn't needed.
 - **Account for mixed-signal accelerator sensitivity**: Analog and mixed-signal AI accelerators exhibit performance that varies with process, voltage, and temperature (PVT). This is usually mitigated during hardware-aware training, but it still matters at inference in deployed systems—accuracy and energy can drift as temperature or supply voltage change. Prefer routing work to such accelerators within their validated operating envelope, and in a heterogeneous system fall back to a more robust (e.g. digital) compute unit when conditions move outside it.
 - **Schedule across heterogeneous compute units**: With CPUs, GPUs, NPUs, and non-von-Neumann accelerators increasingly present on the same edge device, treat scheduling as a placement decision—dispatch each request to the unit offering the best energy-per-inference for the current accuracy target and operating conditions, not just to a single default processor.