Program optimization is needed in order to achieve highly efficient execution of deep learning models trained with the deep learning framework on the R-Car V4H and to realize real-time inference processing. Specifically, this involves program conversion for high-speed computation using CNNIP, an accelerator for deep learning that is equipped in R-Car, and memory optimization to maximize the utilization of the high-speed, small-capacity SRAM installed in R-Car. Manually performing this kind of optimization is extremely difficult and requires a great deal of man-hours, since it requires a deep understanding of the target hardware.
This tool provides the ability to generate a fast executable program by taking a trained deep learning model as input and automatically applying the optimizations for R-Car V4H. This tool was developed by adding a backend for R-Car V4H to the OSS Apache TVM, so performance optimization for R-Car V4H can be applied in the same way as compiling for a CPU and GPU.
Software title
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Software type
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Company
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R-Car NAS (Neural Architecture Search) Tool for automatically designing deep learning models that run efficiently on R-Car
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Model-Based Development | Renesas |
R-Car DNN Simulator High-speed simulator for deep learning model programs for R-Car
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Simulator | Renesas |
Hybrid Compiler Common interface across generations of SoCs.
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Compiler/Assembler | Renesas |
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Renesas Strategy for Automotive Software | Blog Post | Jan 31, 2023 |
Renesas and Fixstars to Jointly Develop Tools Suite that Optimizes AD and ADAS AI Software for R-Car SoCs | News | Dec 15, 2022 |