Zephyr Power & Performance

SkillDocs & knowledge

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.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Zephyr Power & Performance skill

What this skill tells your AI

The instructions your AI receives, as published by beriberikix/zephyr-agent-skills in skills/power-performance/SKILL.md and read by ahel’s review.

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.

Signals

GitHub stars
63
Forks
15
Last commit
May 2026
Advanced
Catalog kind
skill
Gateway key
power-performance
Source
github.com/beriberikix/zephyr-agent-skills