![]() Use these new configurations after downloading them and applying an overlay patch on top of NVIDIA JetPack 5.0.2 per the instructions found inside the downloaded file.įor more information about the flashing configurations useful for emulation, see Emulation Flash Configurations.Īfter flashing is complete, complete the initial bootup and configuration. Flash.sh commands for Jetson Orin modulesįlash configurations for Jetson Orin Nano modules are not yet included in NVIDIA JetPack, as of version 5.0.2. flash.sh jetson-agx-orin-devkit-as-nano4gb mmcblk0p1 flash.sh jetson-agx-orin-devkit-as-nano8gb mmcblk0p1 flash.sh jetson-agx-orin-devkit-as-nx8gb mmcblk0p1 flash.sh jetson-agx-orin-devkit-as-nx16gb mmcblk0p1 flash.sh jetson-agx-orin-devkit-as-jao-32gb mmcblk0p flash.sh jetson-agx-orin-devkit mmcblk0p1 Table 1 lists the Jetson Orin modules and the flash.sh command appropriate for each. flash.sh jetson-agx-orin-devkit-as-nx-16gb mmcblk0p1 For example, to emulate a Jetson Orin NX 16GB module, use the following command: $ sudo. The exact command that you use should be modified with the name of the flash configuration appropriate for your targeted Jetson Orin module being emulated. For example, the following command flashes the developer kit with its default configuration: $ sudo. After placing your developer kit in Force Recovery Mode, the flash.sh command-line tool is used to flash it with a new image. We provide flashing configuration files for this process.Įmulating Jetson Orin module on the Jetson AGX Orin Developer Kit, follows the same steps as mentioned in to flash a Jetson AGX Orin Developer Kit using the flashing utilities. With one step, you can transform a Jetson AGX Orin Developer Kit into any one of the Jetson Orin modules. ![]() Transform the Jetson AGX Orin Developer Kit into any Jetson Orin module Whether you are developing for robotics, video analytics, or any other use case, the capability of this one developer kit brings many benefits. This also simplifies CI/CD infrastructure. The developer kit can accurately emulate the performance of any Jetson Orin modules by configuring the hardware features and clocks to match that of the target module.ĭevelopment teams benefit from the simplicity of needing only one type of developer kit, irrespective of which modules are targeted for production. The developer kit’s ability to natively emulate performance for any of the modules lets you start now and shorten your time to market. You can begin development today for any Jetson Orin module using the Jetson AGX Orin Developer Kit. This shared architecture means you can develop software for one Jetson Orin module and then easily deploy it to any of the others. ![]() With up to 40 TOPS of AI performance, Orin Nano modules set the new standard for entry-level AI, just as Jetson AGX Orin is already redefining robotics and other autonomous edge use cases with 275 TOPS of server class compute.Īll Jetson Orin modules and the Jetson AGX Orin Developer Kit are based on a single SoC architecture with an NVIDIA Ampere Architecture GPU, a high-performance CPU, and the latest accelerators. VPI - CUDA performance Algorithmġ280x720 -> 1920x1080 RGBA8, linear interp.With the Jetson Orin Nano announcement this week at GTC, the entire Jetson Orin module lineup is now revealed. VPI - CPU performance Algorithmġ280x720 to 1920x1080, RGBA8 linear interp.īoth OpenCV and VPI benchmarking use one stream for algorithm execution. Jetson AGX Xavier CPU comes with eight cores, and Jetson AGX Orin CPU with twelve. The advantage for VPI in this case is that performance increases linearly with the number of additional cores added, whereas OpenCV's single-thread algorithms performance will be unchanged. On the other hand, VPI CPU algorithm performance scales linearly with the number of parallel instances. The main implication is that the OpenCV algorithms that only use one core can have several instances running in parallel, up to the number of CPU cores, without affecting their performance. This is a contrast with VPI, where all available CPU cores are always used. Many OpenCV algorithms once dispatched make use of multiple CPU cores during execution, but some others might not. This version matches the OpenCV version shipped with NVIDIA® JetPack™.Īll plots use logarithmic scale due to the large difference between different algorithm performance numbers.īoth OpenCV and VPI measurements are done using one dispatch thread. ![]() The numbers show that VPI provides a significant speed up in many use cases.Ĭomparison made with OpenCV 4.5.4 built with NVIDIA® CUDA® support enabled. Performance numbers were collected following the method described in Benchmarking Method.īenchmarking was done on NVIDIA® Jetson AGX Orin™ devices, with clock frequencies maxed out. In this section we compare VPI's performance with other well-known Computer Vision libraries.
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