Nvidia’s New Orin Nano Developer Kit: Like Raspberry Pi for Artificial Intelligence
Nvidia’s Jetson motherboard lineup is not typical Raspberry Pi alternate options. Nvidia’s focus is on entry-level AI-based robotics, drones and cameras. Its newest motherboard, the $499 Jetson Orin Nano, boosts processing energy whereas maintaining the equipment compact.
Jetson Orin Nano enhances the 128 CUDA cores of Nvidia Maxwell GPU with 1024 Amps primarily based CUDA cores. The additional cores and newer structure imply the Orin Nano has as much as 80 occasions the AI ​​efficiency of the Jetson Nano. Six Arm A78AE CPU cores present almost seven occasions the efficiency of the Jetson Nano. The identical AI structure that powers the Jetson AGX Orin module is used within the Orin Nano, however at a way more inexpensive value.
Nvidia despatched me a pattern of the Jetson Orin Nano for my evaluate, however as a consequence of some logistical points, the unit arrived too quickly for a full evaluate, so I am going to make a quick introduction to the board right here and clarify what I discovered. Sadly, I wasn’t in a position to do any testing with Orin Nano’s major use case, inference and machine studying, as a result of what I attempted on this beta-level software program did not work for me within the restricted time I had. After we hope to acquire a more recent model of Orin Nano’s software program, we are going to publish a full evaluate with inference standards in a number of days.
Be aware that the JetPack software program included with the motherboard is an unique preview and doesn’t mirror the ultimate software program that might be obtainable to customers. I’ll present a full evaluate of Orin Nano, together with its highly effective AI capabilities, when the ultimate software program model turns into obtainable.
Jetson Orin Nano Options
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| Header Cell – Column 0 | Jetson Orin Nano | jetson nano |
|---|---|---|
| Processor | 6-core Arm Cortex-A78AE v8.2 64-bit CPU | Quad-core ARM Cortex-A57 MPCore processor |
| 1.5MB L2 + 4MB L3 | ||
| GPU | Nvidia Ampere structure with 1024 Nvidia CUDA cores and | Nvidia Maxwell structure with 128 Nvidia CUDA cores |
| 32 tensor cores | ||
| Reminiscence | 8GB 128-bit LPDDR5 | 4GB 64-bit LPDDR4, 1600MHz 25.6GB/s |
| 68GB/s | ||
| to retailer | Micro SD | 16GB eMMC 5.1 |
| NVMe M.2 by way of Provider Card | Micro SD | |
| Energy | 7W – 15W (9-19V) | 20W (Max. 5V at 4 Amps) |
| dimensions | 69x45x21mm | 69.6 x 45 x 20mm |
Jetson Orin Nano Provider Card Options
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| Header Cell – Column 0 | Jetson Orin Nano | jetson nano |
|---|---|---|
| Digital camera | 2x MIPI CSI-2 22-pin Digital camera Connectors | 12 lanes (3×4 or 4×2) MIPI CSI-2 D-PHY 1.1 |
| M.2 Key M | x4 PCIe Gen 3 | |
| x2 PCIe Gen3 | ||
| M.2 Key E | PCIe (x1), USB 2.0, UART, I2S and I2C | 1 computer |
| USB | 4 x USB 3.2 Gen2 | 4 USB 3.0 |
| 1 x Kind C for debug and gadget mode | 1 x USB 2.0 Micro-B | |
| networking | Gigabit Ethernet | Gigabit Ethernet |
| RTL8822CE 802.11ac PCIe Wi-fi Community Adapter | ||
| view | Show Port 1.2 | HDMI 2.0 and eDP 1.4 |
| GPIO | 40 Pin GPIO | 40 Pin GPIO |
| 12 Pin Button Head | ||
| 4-Pin Fan Head | ||
| Energy | DC 9-19V Barrel Jack | DC Barrel Jack 20W (Max. 5V at 4 Amps) |
| dimensions | 100 x 79 x 21 mm (peak together with Orin Nano module and cooling answer) | 100 x 80 x 29 mm (Top consists of Jetson Nano module and cooling answer) |
At a look, the Orin Nano and Jetson Nano look the identical. What places the Orin Nano away is the shortage of a fan and HDMI port constructed right into a heatsink. The USB-C port replaces the Jetson Nano’s micro USB. The aforementioned fan is whisper quiet even when working at full 15W. We ran one among Nvidia’s beneficial inference benchmarks and the fan was quiet in contrast to different followers we have examined on the SBCs.
Inference Take a look at
Proper now this chapter is brief and never very candy. I could not confirm that Nvidia claims the Orin Nano delivers virtually 30x the efficiency of the Jetson Nano (hoping to get 45x).
The principle causes for this are a short while scale and particular software program construction. I needed to point out an instance of Hi there AI World utilizing Raspberry Pi Digital camera Module 2, however I bumped into digicam points the place the software program encoder was not detecting the digicam despite the fact that it was listed as suitable. These points have been escalated to Nvidia and I hope a future model of JetPack OS will resolve these points.
Desktop Expertise
Working JetPack 5, a customized model of Ubuntu 20.04, 8GB of LPDDR5 and a six-core Arm CPU present sufficient energy for common desktop duties. Nonetheless, we don’t advocate investing $500 on this motherboard simply to make use of it as a desktop pc.
Preliminary boot was a bit slower than we would hoped, however Nvidia famous in its evaluate information that closing manufacturing models will not have this problem. One other problem we recognized was that solely 6.3GB of RAM was obtainable within the preview construct. The total 8GB might be made obtainable to finish customers by way of a repair. The Ubuntu expertise was nice with minimal quantity of customization accomplished on the desktop, aside from putting in customized instruments to Orin Nano’s strengths.
Chromium took somewhat longer to put in than we anticipated. He apparently put in the scanner by way of Snap, Canonical’s most well-liked packaging platform. Name us old style, however we nonetheless have numerous love for APT.
After the set up was full, we opened Chromium after which went to YouTube to look at some HDR and 4K movies. The primary was LeePSPVideo’s HDR video check, the place we set it to fullscreen and 1440p. Video playback was nice, because the stats for geeks confirmed only a few frames dropped for 1440p 30fps video.
If we did not use the stats for the cows, we might by no means have observed. The subsequent video, a visit round Costa Rica and its wildlife, was performed in 1440p full display screen, however this 60fps video was worse. He dropped about 4% of the frames all through his whole run, the overwhelming majority at the start of the video. Regardless of this problem, playback was nice.
What’s lacking from the Orin Nano is a devoted {hardware} encoder (NVENC). As a substitute, Nvidia presents a software program encoder that makes use of a six-core Arm A78AE CPU. This looks as if a downgrade from the Jetson Nano, however perhaps two further Arm CPU cores are there to make up for it?
The absence of a {hardware} encoder additionally impacts how we use a digicam with the Orin Nano. There are two 15-pin CSI connectors on the left aspect of the provider board. These are suitable with CSI cables manufactured for Raspberry Pi Zero. We related a Raspberry Pi Digital camera Module 2 to CAM0 and examined a fast script to file video. Sadly this could not be in our OS preview construct. Though the IMX219 sensor of Raspberry Pi Digital camera Module 2 is suitable, we couldn’t get a picture.
Utilizing GPIO
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The Orin Nano’s 40-pin GPIO is on the fitting aspect of the provider board and right here is our first problem. What pins are we connecting to? On the Jetson Nano, we screen-printed the board reference subsequent to the pins.
For Orin Nano, we should flip the board over and present psychological ingenuity to recollect the place every pin is. This was mixed with Python examples utilizing a Broadcom (BCM) mapping (Raspberry Pi additionally makes use of BCM mappings in all of its official tutorials), which requires extra decoding. The Python module is RPi.GPIO, a module that Raspberry Pi followers might be effectively conscious of. Created by Ben Croston, this Python module has powered 1000’s of Pi tasks and fairly a number of Jetson tasks. The module is about to run on Jetson boards and is as acquainted to these eyes as ever. To get round BCM to BOARD pin mappings, we selected bodily (BOARD) pin mappings, regardless of our a few years of expertise instructing Raspberry Pi-based content material.
It labored and we had a blinking LED. GPIO pins additionally present the same old plethora of communication protocols. From easy digital IO to UART, SPI, I2C and I2S. Orin Nano’s GPIO isn’t the main focus of the board, however a further characteristic for many who wish to mix machine studying with robotics or an array of sensors.
Nvidia’s Jetson Orin Nano developer equipment is at the moment obtainable by approved distributors for $499.
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