DRP-AI Extension Pack (Pruning Tool)

DRP-AI for RZ/V2H and RZ/V2N supports a feature for efficiently calculating the pruned AI model. The DRP-AI Extension Pack provides a pruning function optimized for RZ/V2H and RZ/V2N. The DRP-AI optimized pruning function can be used in combination with this tool and PyTorch or TensorFlow training code.

What is pruning?

Nodes in a neural network are interconnected as shown in the figure. Methods of reducing the number of parameters by removing weights between nodes or removing nodes are referred to as “pruning”. A neural network to which pruning has not been applied is generally referred to as a dense neural network. And a neural network to which pruning has been applied is generally referred to as a sparse neural network. Applying pruning leads to a slight deterioration in the accuracy of the model but can reduce the power required by hardware and accelerate the inference process.

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How to embed the pruned model

The pruned model can be embedded using DRP-AI TVM. Refer to the DRP-AI TVM page on GitHub for details on TVM.
https://github.com/renesas-rz/rzv_drp-ai_tvm

Note: As shown in the figure, pruning is an optional function. (Dense model also can be embedded.)

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