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RZ/V2H Quad-core Vision AI MPU Evaluation Kit
The RZ/V2H AI MPU evaluation kit (RTK0EF0168C04000BJ) is used to evaluate Renesas' RZ/V2H quad-core vision AI MPU. The kit includes a CPU board and an expansion (EXP)...
Overview
Description
DRP-AI for RZ/V2H supports a feature for efficiently calculating the pruned AI model. The DRP-AI Extension Pack provides a pruning function optimized for RZ/V2H. 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.
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
- 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
Design & Development
Related Boards & Kits
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