Key Optimization Technologies

1. Model structure optimization
   • Model Compression
     • Pruning: Reduce model size by removing less important neurons or weights.
     • Quantization: Convert 32-bit floating point operations to 16-bit or 8-bit integers to reduce the model size and computation amount.
   • Lightweight model design
     • Design of lightweight neural network structures.
     • The balance between accuracy and speed through hierarchical design.
2. Hardware Optimization
   • GPU/TPU Utilization
     • Using hardware acceleration libraries.
   • Edge device optimization
     • Optimized in limited computing environments.
     • Deployment of custom models for each hardware.
3. Learning Optimization
   • Hyperparameter Tuning
     • Improve learning efficiency and performance by optimizing parameters.
   • Intelligent Learning Techniques
     • Transfer Learning: Reduces learning time by utilizing pre-trained models.
     • Knowledge Distillation: Transferring knowledge from large models to lightweight models.
4. Deployment and execution optimization
   • Model conversion and runtime optimization
     • Convert models into executable models on various platforms.
     • Deploying lightweight models.

Tested devices: Jetson Orin Nano, Jetson Orin NX, Jetson AGX Orin, Hailo
Performance: Range from 3x to 20x or even higher, depending on the optimization techniques applied, the type of model, and the target hardware.

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