NVIDIA Xavier - JetPack 5.0.2 - Components - CuDNN

NVIDIA®Jetson Xavier™
Introduction
SoM Overview Carrier Boards NVIDIA Xavier Developer Kit Connect Tech Rogue Connect Tech Mimic Software Support NVIDIA JetPack
JetPack 5.0.2
Getting Started Installing JetPack Flashing the board with GUI installer JetPack Components Compiling Source Code Flashing the Board from Cmdline Performance Tuning Evaluating Performance Set Values Manually Tuning Power Maximizing Performance
Development in the Board
Getting into the Board Using Serial Console Using SSH Using Ubuntu Login Installing Packages
Video Capture and Display
Description Supported Cameras Imx219 Driver Software Support Using GStreamer Using Libargus
Processors
HD Audio and Video Subystem Video Encoder Video Decoder JPEG Encoder and Decoder Video Image Compositor Audio Processing Engine Deep Learning Accelerator Volta GPU Description Using CUDA Using OpenGL
Interfaces
I2C SD/eMMC Controller USB Controller
GStreamer Pipelines
Capture and Display H264 H265 VP9 MPEG4 Simultaneous Streams Compositor
Deep Learning
TensorRT Using TensorRT integrated with Tensorflow Parsing a Tensorflow model for TensorRT Parsing Caffe model for TensorRT Building TensorRT API examples Deep Learning Accelerator Software Support Deep Learning Tutorials Jetson Inference Jetson Reinforcement
RidgeRun Products
GstPTZR GstCUDA GstColorTransfer GstWebRTC GstRtspSink GstInterpipe GstInference GStreamer Daemon GstShark
Reference Documentation
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cuDNN is a library for deep learning acceleration. Most of the more popular deep learning frameworks use cuDNN as their default GPU implementation, or at least is an available option. cuDNN runs around all NVIDIA GPU platforms, which means that to run a cuDNN application developed on a host you just need to recompile on the new platform. cuDNN is optimized for each NVIDIA GPU architecture, so that performance is not affected by a change of the architecture.