Micro-Joule Attribution Engine (MJAE)

Precise energy attribution for AI workloads across platforms.

Pitch

Micro-Joule Attribution Engine (MJAE) transforms raw telemetry into a signed AmalgafySeal, delivering verifiable measurements of energy consumption for individual processes in diverse environments. Optimized for data centers and clusters, MJAE ensures accurate auditing and accountability of energy usage at a granular level.

Description

The Micro-Joule Attribution Engine (MJAE) is a powerful cross-platform systems daemon designed to create a Trust Layer for AI energy auditing. This innovative tool operates across various environments—including heterogeneous data centers, Linux H100 clusters, Windows Server farms, and macOS local development setups—transforming raw, kernel-level telemetry into a signed AmalgafySeal. This seal serves as a validated proof of energy consumption, indicating precisely how many micro-joules each process utilized and specifying the physical machine that executed the process.

"Anyone can say the GPU used 300 W. Only MJAE can say Process 1024 used 287 W of it because it saturated the 16-bit float units — and that this measurement came from SoC die XYZ."

Capabilities

MJAE integrates seamlessly with multiple operating systems, leveraging bespoke telemetry sources to deliver precise energy attribution:

Surface	Backend	Telemetry Source
Linux	`LinuxProvider`	Utilizes Aya eBPF hooks to monitor GPU activity windows and process switching, alongside package power consumption data from `/sys/class/powercap/intel-rapl:0/energy_uj`
Windows	`WindowsProvider`	Employs eBPF-for-Windows to track process lifecycles while capturing telemetry from Direct3D/CUDA and NVML
macOS	`AppleSiliconProvider`	Collects energy data via raw IOReport FFI, monitoring the Apple Silicon SoC power planes

Each provider adheres to the EnergyProvider trait, ensuring that the daemon's main loop remains OS-agnostic.

Architectural Overview

MJAE features a robust architecture designed for efficient energy sampling and attribution:

┌────────────┐  ┌────────────┐  ┌────────────┐
│ LinuxProv. │  │ WinProv.   │  │ AppleProv. │
└─────┬──────┘  └─────┬──────┘  └─────┬──────┘
      │ 100 ms tick   │ 100 ms tick   │ 100 ms tick
      │ Δ energy      │ Δ energy      │ Δ energy
      ▼               ▼               ▼
        ┌───────────────────────────────┐
        │     AmalgafyRegistry          │  ← lock-free, ordered,
        │     PID → cumulative µJ       │     process-wide singleton  
        └────────────────┬──────────────┘
                         │  snapshot()
                         ▼
                ┌─────────────────┐    ┌──────────────────┐
                │  AmalgafySigner │ ←─ │ HardwareIdentity │
                │  Ed25519 + JCS  │    │ (IOKit / m-id /  │
                └────────┬────────┘    │  NVML UUID)      │
                         ▼             └──────────────────┘
                  AmalgafySeal  ← published every 60 s,
                                  cryptographically bound to
                                  the host's hardware fingerprint

Detailed Functionality

Unified Registry: The AmalgafyRegistry acts as the central repository for energy attribution, using efficient data structures to allow quick and accurate updates.
Differential Sampling Daemon: Each provider integrates a default implementation that executes a non-blocking 100 ms sampling loop, ensuring real-time energy consumption tracking without interruptions to system performance.
Linux eBPF integration: Incorporates user-space features for monitoring the energy attributed to various processes, while allowing operators flexibility in configuring kernel-level hooks.
Hardware Identity & Audit-Spoof Resistance: The system captures unique hardware fingerprints across platforms to ensure data authenticity, thereby preventing unauthorized access and spoofing.
Deterministic Attribution Model: Uses advanced math to calculate attributed energy accurately, incorporating real-time data adjustments and fallback mechanisms to maintain integrity.
Amalgafy Seal: Generates a digitally signed seal at regular intervals to ensure that energy consumption data is verifiable and securely tied to specific hardware identifiers.

Example Usage

Here is a brief programming example of how to interact with the MJAE framework:

use mjae::global_registry;

let registry = global_registry();
registry.add_micro_joules(1024, 287_000); // Process 1024: +287 mJ
let total = registry.total_micro_joules();

MJAE is designed for embedding within tokio-based systems, ensuring that performance and efficiency are maximized in applications aiming for precise energy management and auditing in AI and high-performance computing environments.