power-performance

name	power-performance
description	Power management and performance optimization for Zephyr RTOS. Covers system power states (Idle, Suspend, Off), device-level power management, residency hooks, and code/data relocation for speed efficiency. Trigger when optimizing battery life, reducing latency, or managing memory constraints.

Zephyr Power & Performance

Maximize the efficiency of your embedded system by balancing power consumption and computational performance.

Core Workflows

1. Power Management (PM)

Implement system-level and peripheral-specific power saving strategies.

• Reference: power_management.md
• Key Tools: pm_device_action_run, pm_state_set, Residency hooks.

2. Performance Tuning

Optimize critical code paths and monitor system resources.

• Reference: performance_tuning.md
• Key Tools: CONFIG_THREAD_ANALYZER, Linker Map, Code relocation.

3. Memory Optimization

Relocate code and data to utilize the fastest memory available.

• Reference: performance_tuning.md
• Key Tools: __ramfunc, Relocation scripts.

Quick Start (Device Suspend)

#include <zephyr/pm/device.h>

const struct device *spi0 = DEVICE_DT_GET(DT_NODELABEL(spi0));

void sleep_spi(void) {
    pm_device_action_run(spi0, PM_DEVICE_ACTION_SUSPEND);
}

Professional Patterns (Optimization)

• Aggressive Suspend: Transition peripherals to low-power states as soon as their transaction is complete.
• ITCM/DTCM: Use Tightly Coupled Memory for time-critical control loops to avoid Flash latency.
• Runtime Monitoring: Always enable the thread analyzer during development to find the "RAM floor" for your application.
• Coordinated Sleep: To coordinate sleep across modules, see kernel-services for Zbus-based event-driven power management.

Automation Tools

• power_budget_estimator.py: Estimate average current and battery life from duty-cycle state data.

Examples & Templates

• power_budget_template.csv: Starter power-state budget sheet for battery-life estimation.

Validation Checklist

• Target peripherals enter and exit suspend/resume states without functional regressions.
• Measured idle and active power align with expected optimization deltas.
• Thread analyzer and map data confirm stack/RAM budgets are within limits.
• Relocated time-critical functions execute from intended memory region.

Resources

• References:
  • power_management.md: System states, device PM, and hooks.
  • performance_tuning.md: Optimization strategies and relocation.
• Scripts:
  • power_budget_estimator.py: Duty-cycle based battery-life estimator.
• Assets:
  • power_budget_template.csv: Initial state/current budget template.