Edge AI Inference Platform

Deploy and manage ML models at the manufacturing edge — OPC-UA integration, sub-millisecond inference, fleet management

The Problem

Cloud ML inference introduces latency (100-500ms), requires constant connectivity, incurs data egress costs, and creates security risks in air-gapped environments. Edge deployment solves this with <5ms latency, offline capability, and zero cloud dependency.

This platform provides:

• Multi-hardware support: NVIDIA Jetson, Intel NUC, Raspberry Pi 5, industrial PCs
• Sub-5ms inference latency on defect detection models
• ONNX/TensorRT optimization with hardware-specific acceleration
• OPC-UA integration exposing inference results to SCADA/MES
• Fleet management: 100+ edge devices from single dashboard
• OTA model updates with automatic rollback on degradation
• Network segmentation for IT/OT security

Architecture

graph LR
    A["ML Models<br/>Cloud-trained"] --> B["ONNX/TensorRT<br/>Export"]
    B --> C["Edge Device<br/>Manager"]
    C --> D["Edge Device 1<br/>Jetson/NUC"]
    C --> E["Edge Device N<br/>Industrial PC"]
    D --> F["OPC-UA Server<br/>SCADA/MES"]
    E --> F
    G["Real-time<br/>Data"] --> D
    G --> E
    F --> H["Operator<br/>Dashboard"]

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Key Capabilities

1. Edge Runtime Engine

• ONNX model loading with TensorRT acceleration
• Dynamic batching for throughput optimization
• Model hot-reload without downtime
• Inference queue with priority scheduling
• Latency SLAs enforcement (<5ms for safety-critical)

2. OPC-UA Server

• Custom manufacturing node namespace
• Real-time inference result publishing
• Historical data archiving (HA)
• Certificate-based security
• Event-based notifications

3. PLC Connector

• Protocols: Modbus TCP/RTU, EtherNet/IP, PROFINET, S7
• Real-time parameter reading and setpoint writing
• Ladder logic integration hooks
• Fail-safe defaults on communication loss

4. Fleet Monitor

• Centralized device health dashboard
• 100+ concurrent device management
• Remote configuration and troubleshooting
• OTA update orchestration
• Alert system for device/model degradation

5. Model Compression

• INT8 quantization with <0.5% accuracy loss
• Pruning for 50-70% model size reduction
• Knowledge distillation from teacher models
• Latency vs accuracy Pareto optimization

6. Security Framework

• Secure boot verification
• Model encryption at rest and in transit
• Network segmentation enforcement
• Device authentication and revocation

Performance Targets

• Inference latency: <5ms for defect detection
• Model update deployment: <60 seconds fleet-wide
• Device availability: >99.9% uptime
• Network efficiency: <50MB/day per edge device

Installation

pip install -e .

# On edge device, start runtime:
python -m edge_ai_inference_platform.runtime.edge_runtime \
    --models-dir /path/to/models \
    --opc-ua-endpoint opc.tcp://0.0.0.0:4840

Project Structure

edge-ai-inference-platform/
├── src/
│   ├── runtime/
│   │   ├── edge_runtime.py
│   │   └── model_registry.py
│   ├── connectivity/
│   │   ├── opc_ua_server.py
│   │   └── plc_connector.py
│   ├── monitoring/
│   │   └── fleet_monitor.py
│   ├── optimization/
│   │   └── inference_optimizer.py
│   └── security/
│       └── edge_security.py
├── examples/
│   ├── deploy_defect_detector.py
│   └── opcua_inference_server.py
├── tests/
│   └── test_edge_runtime.py
├── docs/
│   └── HARDWARE_GUIDE.md
├── pyproject.toml
├── LICENSE
├── .gitignore
└── CONTRIBUTING.md

License

MIT License - see LICENSE for details.