AI Anomaly Detection On-Device Roadmap
This document outlines the current roadmap for the development of an on-device AI anomaly detection system using ONNX models. The system is designed to work entirely offline without cloud dependencies.
Project Overview
- Goal: Enable embedded Linux devices to detect anomalies in real-time using AI models.
- Scope: On-device inference only; no cloud integration at this stage.
- Model Format: ONNX (Open Neural Network Exchange) to allow flexible model updates and runtime integration.
- Use Cases: Predictive maintenance, sensor anomaly detection, and general-purpose monitoring.
Current Status (In Progress)
Core Components
-
ONNX Runtime Integration
- Developing a C-based runtime system to load and execute ONNX models.
- Support for CPU-only inference on embedded devices.
-
Lua-Configurable Data Acquisition
- Handlers implemented in Lua to acquire sensor or event data.
- Configurable through uMINK plugin system.
-
Preprocessing and Feature Extraction
- Transform raw sensor data into model-compatible input tensors.
- Configurable pipeline in Lua for flexibility.
-
Model Execution & Inference
- Run ONNX models on-device and output anomaly scores or classifications.
- Lightweight logging of model inference results.
-
Signal Integration
- uMINK signal handlers wrap AI model execution.
- Allows chaining signals and reporting results internally.
Planned Enhancements
- Persistent Model Storage: Support for updating models from a local source.
-
Performance Monitoring: Integration with
M.perf_*
functions to track inference latency and error rates. - Modular Deployment: Package as a reusable uMINK plugin for multiple devices.
Development Milestones
Milestone | Target Date | Status |
---|---|---|
ONNX Runtime Integration | Q3 2025 | In Progress |
Lua Data Acquisition Handlers | Q3 2025 | In Progress |
Feature Preprocessing Pipeline | Q3 2025 | In Progress |
Model Inference & Scoring | Q3 2025 | In Progress |
Integration with uMINK Signals | Q3 2025 | Planned |
Performance Monitoring & Metrics | Q4 2025 | Planned |
Model Update & Persistence | Q4 2025 | Planned |
Notes
- No cloud dependencies: All AI computations and data storage occur locally on the device.
- Extensibility: The system is designed to allow new anomaly detection models to be added easily via ONNX files.
- Target Devices: Low-power embedded Linux devices with modest CPU and memory constraints.
This roadmap is actively evolving as development progresses, with focus on lightweight, modular, and fully on-device AI anomaly detection.