Your AI agent works great. Then the phone gets warm. Then it doesn’t.

Описание и применение

На устройствах Android постоянный запуск модели вызывает перегрев, что приводит к снижению производительности. Предварительное измерение теплового headroom и адаптивное снижение нагрузки позволяют избежать резкого падения скорости работы агента.

Задача

On-device AI agents can become slow after a few minutes of continuous inference due to thermal throttling, which is not evident during testing.

Результат

Proactive throttling based on real‑time thermal headroom prevents sudden latency spikes and maintains a smoother user experience on devices prone to thermal throttling.

Рабочий процесс

• 1. Call PowerManager.getThermalHeadroom(1) to obtain a 1‑second forecast of available thermal headroom.
• 2. If headroom < 0.3 f, decide on an action: < 0.2 f → switch to a lighter model; < 0.3 f → reduce inference frequency; < 0.5 f → skip non‑critical inference.
• 3. Implement the chosen action in the inference loop.
• 4. Continuously monitor headroom during sustained inference.
• 5. Test on a 3‑4 year‑old mid‑range device for 10 min to observe latency trends.
• 6. Use Firebase Test Lab to identify affected devices in the target market.

Доказательства

Android’s thermal system throttles CPU and GPU frequency when the device temperature rises.

Источник

PowerManager.getThermalHeadroom() gives you a 1-second forecast of available thermal headroom, from 0.0 (throttling now) to 1.0 (full capacity).

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When headroom drops below your threshold: ... switchToLighterModel(), reduceInferenceFrequency(), skipNonCriticalInference()

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Run sustained inference on a 3-4 year old mid-range device for 10 minutes. Watch the latency numbers. If they climb, you have a thermal problem.

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