Ultrasound: AI applications can improve ultrasound accuracy through improved image optimization, analysis, and interpretation. This can help better diagnose conditions such as cancerous tumors or abnormalities during pregnancy. Right now this section is short, and not very sweet. Nvidia’s claims that the Orin Nano delivers almost 30 times the performance of the Jetson Nano (that it hopes to improve to 45 times) I was unable to verify. The NVIDIA® Jetson AGX Orin™ Developer Kit and all Jetson Orin modules share one SoC architecture, enabling the developer kit to emulate performance and power for any of the modules. It is configured by default for those targeting Jetson AGX Orin series modules, but can be easily re-flashed to emulate Jetson Orin NX or Jetson Orin Nano series modules.

Connect NVIDIA Jetson AGX Orin Developer Kit to the PC with the bundled USB Type-A to Type-C cable. Field inspection: The combination of image recognition and processing and automated reporting, makes field inspections more accurate, safe, and efficient. Figure 3. Results from benchmark testing with pretrained models comparing the Jetson Nano to the Jetson Orin Nano 8 GB Your developer kit should automatically power on, and the white LED ( 0 ) near the power button will light. If not, press the Power button ( 1 ). NVIDIA Jetson AGX Orin Developer Kit: smallest and most powerful AI edge computer. The NVIDIA® Jetson AGX Orin™ Developer Kit is built with high-performance, power-efficient Jetson AGX Orin module, delivering up to 275 TOPS and 8X the performance of NVIDIA® Jetson AGX Xavier™ in the same compact form-factoredCollision detection: AI can analyze real-time video and image processing to determine if the movement of machines and humans may lead to a collision. It can then take safety actions to avoid the incident. Figure 2. The Jetson Orin Nano Developer Kit includes a Jetson Orin Nano 8 GB compute module, reference carrier board, heatsink/fan, DC power supply, and 802.11ac/abgn WLAN+BT module Jetson Orin Nano does not include a NVEncoder. For Table 3, encoding for Jetson Orin Nano was done on CPU using ffmpeg. 110 FPS is achieved when using four CPU cores. When using two CPU cores, FPS of 73 was achieved, and when using a single CPU core, FPS of 33 was achieved. Development for NVIDIA DRIVE AGX Orin, the advanced, software-defined platform for autonomous vehicles

For the Orin Nano, we have to flip over the board and perform a feat of mental dexterity to remember where each pin is. This was compounded by the Python examples using a Broadcom (BCM) mapping (Raspberry Pi also uses BCM mappings in all its official tutorials) which required further decoding. The Python module is RPi.GPIO, a module that Raspberry Pi fans will be intimately aware of. Created by Ben Croston, this Python module has powered thousands of Pi projects, and quite a few Jetson projects too. The module has been tweaked to run on the Jetson boards and is as familiar as ever to these eyes. To get around the BCM to BOARD pin mappings we chose the physical (BOARD) pin mappings, despite our years of experience teaching Raspberry Pi based content. Upon completing the oem-config, the developer kit reboots, and you should see the Ubuntu desktop like this on the display. Medical device manufacturers are embracing new AI-powered capabilities to accelerate TTM and lower development and maintenance costs. Leveraging AI computing platforms enable medical device developers to: Industrial inspection: AI models are trained to detect and classify objects and identify and remove any defective products.Autonomous vehicle developers now have access to flexible, scalable, and high-performance hardware and software to build the next generation of safer, more efficient transportation. The Jetson Orin Nano Developer Kit is a versatile platform that supports models trained with NVIDIA TAO Toolkit 4.0, and will soon support newly announced models in TAO Toolkit 5.0. With TAO Toolkit 5.0, developers can take advantage of several state-of-the-art vision transformer models for image classification, object detection, and segmentation use cases. To learn more, see Access the Latest in Vision AI Model Development Workflows with NVIDIA TAO Toolkit 5.0 . To power down the developer kit, complete the Ubuntu shutdown sequence using either the shutdown button (GUI) or command (CLI).