ONNX Format Advantages

Solves interoperability issues between deep learning frameworks:

  1. Export models from PyTorch, TensorFlow to standard format
  2. Run on different programming languages and hardware platforms
  3. Avoid retraining models for cross-platform deployment

Typical Deployment Process

  1. Model export: Use framework-specific API to convert to ONNX format
  2. Model optimization: Graph optimization, operator fusion, constant folding
  3. Inference engine selection: ONNX Runtime / TensorRT / OpenVINO

Performance Optimization Techniques

  • Quantization compression: FP32 → INT8, reduces memory usage, improves inference speed
  • Batch processing optimization: Balance latency and throughput
  • Hardware-specific optimization: Customized for platforms like Jetson

TensorRT

NVIDIA deep learning inference optimization library, designed for GPU acceleration:

Core Technologies

  1. Tensor fusion: Reduces memory access and kernel launch overhead
  2. Low-precision computation:
    • FP16: 2× throughput improvement
    • INT8: 4× speed improvement
    • INT4: 75% memory reduction

Optimization Process

  1. Model pruning: Structured/unstructured pruning
  2. Quantization deployment: PTQ / QAT

Actual Results

MetricOriginal ModelINT4 QuantizedImprovement
Inference latency120ms35ms3.4×
Memory usage8GB2GB4×
Task success rate92.3%92.7%+0.4%

Triton

NVIDIA Triton Inference Server:

  1. Model parallel deployment: Support loading multiple model versions simultaneously
  2. gRPC/REST API: Standard API, latency <50ms
  3. Decoupled architecture: Decision/perception module separation

Deployment Solution Selection

GPU/NPU Platforms (Jetson)

  • Use TensorRT
  • Layer fusion optimization, INT8 quantization, dynamic tensor memory management

x86/ARM CPU Platforms (Raspberry Pi)

  • Use OpenVINO
  • Model optimizer, CPU extension instruction set, OpenCL acceleration

Containerization

Use Docker to package dependencies, ensuring consistency and portability:

  1. Environment configuration: Create Dockerfile
  2. Dependency management
  3. Build and testing
  4. Deployment optimization
  5. Version control

Technology Stack Summary

  1. Perception layer: LiDAR, camera, IMU (10-100Hz)
  2. Decision layer: ROS/ROS2, sensor fusion, localization and mapping
  3. Execution layer: Motors, robotic arms (millisecond response)