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power-performance

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.

The English summary is being prepared. Showing the original description.

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Files included (6)

  • SKILL.md3.0 KB
  • assets/power_budget_template.csv102 B
  • references/performance_tuning.md1.5 KB
  • references/power_management.md1.9 KB
  • scripts/power_budget_estimator.py3.0 KB
  • skill-meta.yaml504 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

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.

3. Memory Optimization

Relocate code and data to utilize the fastest memory available.

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

Examples & Templates

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.

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