How eBPF and AI Redefine Mobile Microarchitectural Energy‑Efficiency Analysis

Modern mobile systems face three systemic barriers to energy‑efficiency optimization: (1) data collection itself introduces significant load that distorts observations; (2) microarchitectural causes are numerous and tightly coupled, making manual root‑cause analysis infeasible; (3) fragmented toolchains prevent consistent, reusable diagnostics. To overcome these, the authors propose a paradigm shift from "peak performance" to "energy‑efficiency‑first" and introduce a three‑layer architecture.

Layer 1 – Low‑Disturbance Sensing: Using eBPF hooks (e.g., sched_switch, cpu_frequency, irq_handler_entry/exit, cpu_idle) the kernel captures scheduling, DVFS, interrupt, and idle events with millisecond‑level alignment to a high‑precision power‑capture card. Eight PMU events across five attribution buckets are sampled concurrently, and data are aggregated in‑kernel via per‑CPU maps before being exposed to user‑space via a ring buffer, keeping overhead below 0.8%.

Layer 2 – AI Diagnosis: Aligned sensor data are fed to an AI engine that cross‑validates against a chip‑physical energy model, producing task‑level power estimates and microarchitectural bottleneck diagnoses. A double‑window approach—global trend correlation (Pearson) and event‑level causal sequencing—filters noise and isolates high‑value DVFS coupling pairs (e.g., frequency‑load, thermal‑schedule). The AI also performs linear‑regression‑based task‑power modeling using eight PMU features, enforcing energy‑conservation (∑P_task ≈ P_cluster) and keeping inference cost under 3%.

Layer 3 – Agent‑Driven Execution: Diagnoses are handed to a LangGraph‑based multi‑agent system. The pipeline proceeds as follows: (1) Perception & Processing – agents trigger eBPF collection with strict timestamp alignment; (2) Multi‑Level Analysis – agents apply Pearson correlation and static PMU baselines to isolate hotspots; (3) Core Inference – an LLM‑augmented agent with a RAG knowledge base performs causal reasoning to pinpoint wasteful compute paths; (4) Safe Deployment – rules, ML models, and SPRT statistical tests validate optimization actions before they are applied.

The system was evaluated on a short‑video high‑load scenario. End‑to‑end, the eBPF base added less than 0.8% power overhead. After deploying AI‑driven scheduling tweaks and decoder buffer pre‑allocation, frame‑rate stalls vanished, CPU power dropped 15‑20%, and SoC temperature fell by ~2 °C. Overall, the approach promises a 10‑15% battery‑life uplift for flagship devices, with software‑level contributions (system‑software + AI scheduling) accounting for roughly 25% of the gain.

Looking ahead to 2026, the authors identify three drivers for autonomous energy efficiency: (1) eBPF evolving from passive observation to active kernel‑level intervention via Linux 6.7’s sched_ext framework; (2) cross‑layer ecosystem collaboration that quantifies always‑on service wake‑up costs at microsecond granularity; (3) holistic forecasting that could raise flagship endurance by 10‑15%.

In summary, merging eBPF’s lightweight observability with AI’s inference power and an agent‑orchestrated feedback loop transforms the optimization workflow from manual, fragmented troubleshooting to an automated, data‑driven, and safely deployable system.