GPU Coder Interface for Deep Learning
Use GPU Coder to generate optimized CUDA code for deep learning networks
다운로드 수: 2.6K
업데이트 날짜:
2024/3/20
GPU Coder generates optimized CUDA code from MATLAB code and Simulink models for deep learning, embedded vision, and autonomous systems. You can deploy a variety of pretrained deep learning networks such as YOLOv2, ResNet-50, SegNet, MobileNet, and others from Deep Learning Toolbox to NVIDIA GPUs. You can generate optimized code for pre-processing and post-processing along with your trained deep learning networks to deploy complete applications.
When used with GPU Coder, GPU Coder Interface for Deep Learning provides the ability for the generated code to call into cuDNN or TensorRT optimization libraries for NVIDIA GPUs.
When used in MATLAB with Deep Learning Toolbox and without GPU Coder, you can accelerate the execution of deep learning networks on NVIDIA GPUs.
This support package is functional for R2018b and beyond.
If you have download or installation problems, please contact Technical Support - https://www.mathworks.com/support/contact_us.html
MATLAB 릴리스 호환 정보
개발 환경:
R2018b
R2018b에서 R2024a까지의 릴리스와 호환
플랫폼 호환성
Windows macOS (Apple Silicon) macOS (Intel) Linux카테고리
- AI, Data Science, and Statistics > Deep Learning Toolbox >
- Code Generation > GPU Coder >
- MATLAB > External Language Interfaces > C++ with MATLAB > Call C++ from MATLAB >
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