Edge AI Inference Pipeline Architecture

Introduction to Edge AI Architecture

This architecture enables real-time AI inference on edge devices through Model Optimization (TensorRT/ONNX), Edge Runtime (TFLite/DeepStream), and Hybrid Execution with cloud fallback. It supports devices like NVIDIA Jetson, Coral TPU, and Raspberry Pi with components for Model Compression (pruning/quantization), Edge Orchestration (K3s/Eclipse ioFog), Local Decision Logic, and Cloud Sync for model updates. The system handles Data Preprocessing at the edge, Hardware Acceleration (GPU/TPU/VPU), and Offline Capability with intermittent cloud connectivity.

High-Level System Diagram

The pipeline begins with Cloud-Based Training producing models that undergo Edge Optimization before deployment to Edge Devices. Devices run Local Inference with optional Sensor Fusion, sending results to Edge Gateways for aggregation. A Sync Service maintains model version consistency across devices and enables Federated Learning. The Cloud Control Plane monitors device health and manages canary rollouts. Color coding: blue for cloud components, green for edge devices, orange for optimization flows, and purple for data sync.

Key Components

• Model Optimization: TensorRT, ONNX Runtime, TFLite Converter
• Edge Devices: Jetson Nano/Xavier, Coral TPU, Raspberry Pi
• Acceleration Frameworks: DeepStream, OpenVINO, Arm NN
• Edge Orchestration: K3s, ioFog, Azure IoT Edge
• Local Processing: GStreamer pipelines, OpenCV
• Cloud Sync: MQTT/WebSockets for model updates
• Hybrid Logic: Decision trees for cloud fallback
• Device Monitoring: Prometheus Edge Stack
• Security: TPM-based attestation, encrypted models
• Update Strategies: A/B testing, canary deployments

Benefits of Edge AI Architecture

• Low Latency: Sub-50ms inference without cloud roundtrip
• Bandwidth Efficiency: 90%+ data reduction vs. cloud streaming
• Offline Operation: Continuous function during outages
• Privacy Compliance: Sensitive data never leaves device
• Cost Savings: 60-80% lower cloud compute costs
• Hardware Flexibility: Supports diverse accelerator chips

Implementation Considerations

• Model Optimization: INT8 quantization with calibration
• Device Selection: Match compute to model requirements
• Pipeline Design: Overlap capture/preprocessing/inference
• Update Mechanism: Delta updates for constrained bandwidth
• Fallback Logic: Confidence thresholds for cloud handoff
• Monitoring: Edge-optimized metrics collection
• Testing: Hardware-in-the-loop validation
• Security: Secure boot + model encryption

Example TensorRT Optimization

# Convert PyTorch model to TensorRT
import torch
import tensorrt as trt

model = torch.hub.load('pytorch/vision', 'resnet18', pretrained=True)
dummy_input = torch.randn(1, 3, 224, 224).cuda()

# Create TensorRT engine
logger = trt.Logger(trt.Logger.INFO)
builder = trt.Builder(logger)
network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
parser = trt.OnnxParser(network, logger)

# Optimize for Jetson
builder.max_batch_size = 1
builder.max_workspace_size = 1 << 30
builder.fp16_mode = True  # Enable FP16 for Jetson

# Serialize and save
engine = builder.build_cuda_engine(network)
with open('resnet18.engine', 'wb') as f:
    f.write(engine.serialize())
                

Example Edge Deployment Manifest

# edge-deployment.yaml
apiVersion: iofog.org/v3
kind: Application
metadata:
  name: safety-monitor
spec:
  microservices:
  - name: object-detector
    images:
      x86: registry.example.com/trt-detector:x86
      arm64: registry.example.com/trt-detector:jetson
    config:
      model_path: "/models/engine.trt"
      confidence_threshold: 0.7
    resources:
      gpu: 1  # Request Jetson GPU
    ports:
    - external: 5000
      internal: 5000
    volumes:
    - host: "/var/edge-models"
      container: "/models"

# Device selection constraints
  placements:
  - type: constraint
    key: hardware
    operator: ==
    value: jetson
  - type: constraint  
    key: tpu
    operator: exists