Zephyr Power & Performance
SkillDocs & knowledgePower 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.
No other account needed.
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.
- 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.
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