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