Features & Scope of support
NetsPresso Compressor
Compresses models for better computational efficiency
Key Features
Automatic Compression
- Only focus on the compression, not wasting time implementing complicated methods
- Compress PyTorch, TensorFlow models immediately
Structured Pruning, Filter decomposition
- Structured pruning directly improves the inference speed of AI models by reducing the amount of computation
- Filter decomposition decomposes AI models and restores important information
- Fine-tuning after compression is possible to restore the accuracy of the model.
HW aware Model Profiling
- Visualize the neural network to check the structure and available layers to be compressed
- Profile the neural network on the target hardware to decide which layers to compress and how much
Workflow
Scope of support
Framework
- PyTorch (PyTorch version ≥ 1.11)
- PyTorch-ONNX (ONNX version ≥ 1.10)
- PyTorch-GraphModule
- TensorFlow-Keras (TensorFlow version 2.3~2.8)
Compression methods
- Structured Pruning : Pruning by index, Pruning by criteria (L2 Norm, GM, NuclearNorm)
- Filter Decomposition : Tucker Decomposition, Singular Value Decomposition, CP Decomposition
Updated 23 days ago