Overview
Description
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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Features
- Pruning functions optimized for RZ/V2H and RZ/V2N
- Pruning ratio can be specified for balance between accuracy and power efficiency
- Supports 2 pruning modes for improving accuracy (One Shot/Gradual)
Release Information
DRP-AI Extension Pack Version 1.1.0 is available. (Oct. 2024)
- Supported PyTorch models with a multi-head attention structure for a transformer neural network.
Target Devices
Documentation
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| Type | Title | Date |
| Manual - Software | PDF 1.61 MB | |
| Application Note | PDF 440 KB | |
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Design & Development
Related Boards & Kits
Evaluation
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Videos & Training
Overview of DRP-AI TVM
This video provides an overview of DRP-AI TVM, focusing on the integration of AI into "Endpoint" devices for efficient real-time processing. Renesas' DRP-AI acts as a powerful accelerator, offering key features that enhance the performance and capabilities of endpoint AI applications.
Video List
