Overview

Description

DRP-AI Translator is a tool that generates object code for AI from trained AI models, in ONNX format that is executable on Renesas' RZ/V series of MPUs.

It optimizes the graph structure of trained AI models and FP16 quantization to minimize memory accesses and enable highly efficient and fast AI model inference processing. Optionally, you can add pre- or post-processing required for AI, such as normalization and resizing. The added pre- and post-processing is processed by DRP at high speed.

You can easily generate object code that can be implemented on an evaluation board by simply specifying an ONNX file and running the DRP-AI translator on a Linux PC.

Features

The DRP-AI translator has the four features shown in the figure below.

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DRP-AI Translatorの特徴

Conversion flow

The conversion flow with the DRP-AI translator is shown in the figure below.

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DRP-AI Translatorの実装フロー

Software

Software & Tool Pages

DRP-AI Translator [V1.81]This is an AI model conversion tool (DRP-AI Translator) for DRP-AI equipped products. Please check the Release Notes and User's Manual first before using this product. Software Package Renesas
RZ/V2L DRP-AI Support Package [V7.30]This product provides the software and documentation for DRP-AI embedded within RZ/V2L. Please read the release note included in this first when you use this product. Software Package Renesas
RZ/V2M DRP-AI Support Package [V7.30]This product provides the software and documentation for DRP-AI embedded within RZ/V2M. Software Package Renesas
RZ/V2MA DRP-AI Support Package[V7.31]This product provides the software and documentation for DRP-AI embedded within RZ/V2MA. Software Package Renesas

Software Downloads

Title Type Date
ZIP2,551.92 MB
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Software & Tools - Software
ZIP2,816.66 MB
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Software & Tools - Software

Documentation

Title Type Date
PDF327 KB
Release Note
PDF642 KB日本語
White Paper

Videos & Training

Boost Vision AI Power Efficiency Using DRP-AI Accelerator

DRP-AI Accelerator embedded in RZ/V series MPUs provides high-speed AI processing while keeping high power efficiency at the endpoints.