Comparing the Nvidia Jetson embedded boards

06 May.,2024

 

Comparing the Nvidia Jetson embedded boards

Nvidia Jetson embedded boards are one of the most popular choices for a variety of IoT products and applications. In this article we will present the Nvidia Jetson board offering, together with their relevant use cases and specifications.

If you want to learn more, please visit our website Jetson Cameras.

Nvidia Jetson Developer Kits and Nvidia Jetson modules

Before we dive into specific board descriptions, let’s talk a little bit about Nvidia Jetson developer kits. Developer kits include a Jetson module attached to a reference carrier board. They cannot be used for production purposes. Developer kits are used for testing and developing purposes only. Modules, on the other hand, can be used for deployment in production environments. Each Jetson module has no software pre-installed; the user has to attach it to the carrier board designed or procured for the end product, and flash it with the software image they've developed. In this article we will focus on modules only.

Nvidia Jetson Nano

Official Nvidia Jetson Nano page

Nvidia Jetson Nano is a small but powerful board designed for AI purposes. It supports all types of AI models and popular AI frameworks, which allows developers to use it for a variety of applications, such as image classification, segmentation, speech processing, object detection. Nvidia Jetson Nano is equipped with the NVIDIA Maxwell GPU with 128 NVIDIA CUDA® cores and 4 GB memory.

Nvidia Jetson TX2

Official Nvidia Jetson TX2 page

According to the producer, Nvidia Jetson TX2 is the fastest and most power-efficient embedded AI board. Nvidia Jetson TX2 is a supercomputer-on-module, and it is equipped with 256-core NVIDIA Pascal GPU with 256 NVIDIA CUDA cores and 8 GB of memory, which makes it an excellent choice for intelligent edge devices such as robots, drones, intelligent cameras and more.

Nvidia Jetson Xavier NX

Official Nvidia Jetson Xavier NX page

Jetson Xavier NX packs the power of the NVIDIA Xavier SoC into a module of the size of a Jetson Nano. This small AI supercomputer offers more than 10X the performance of Jetson TX2. It’s a great choice for innovative edge devices in industrial applications, retail, agriculture, healthcare, and more. It’s equipped with a 384-core NVIDIA Volta GPU with 48 Tensor Cores and 8 GB of memory.

Nvidia Jetson AGX Xavier

Official Nvidia Jetson AGX Xavier page

Nvidia Jetson AGX Xavier, the latest addition to the Jetson family of modules, provides 20× the performance as compared to the Nvidia Jetson TX2. It’s intended to be used in deep learning and AI applications involving image recognition, object detection, and speech recognition. It is equipped with a 512-core Nvidia Volta GPU with 64 Tensor Cores and 32GB of memory. The Nvidia Jetson AGX Xavier is currently the most powerful module offered by Nvidia.

Mender and Nvidia Jetson use cases

Mender users often choose Nvidia Jetson embedded boards as their modules of choice. We described some of them on our other blog, the Device Chronicle.

Bilberry

A very interesting use case for Nvidia Jetson embedded boards in agriculture is Bilberry. This French company is using Nvidia Jetson hardware with Mender in order to revolutionize agriculture. After extensive research, Bilberry discovered that a very common problem in modern agriculture is the overuse of herbicides. Using the Nvidia Jetson range of products together with Mender’s device management features, they designed an intelligent system that controls application of herbicides. You can click here to read the interview we did with Bilberry’s CTO.

Alcatraz.ai and Boulder AI

Other interesting companies that use Nvidia Jetson embedded boards in their products are Alcatraz.ai and Boulder AI. They both use Nvidia Jetson hardware and Mender to develop futuristic smart-city solutions. Alcatraz.ai designed a facial authentication solution using artificial intelligence. Boulder AI uses deep learning to provide smart city solutions, like a system monitoring vehicle speed in the city of Denver. You can read more about their work by clicking here.

Mender and Nvidia Partnership

Mender is Nvidia Jetson’s preferred partner for remote device management and OTA updates of their embedded modules. Click here to see an overview of the official integration between Nvidia embedded boards and Mender. We also have a very active community taking care of our open-source integration with Nvidia Jetson embedded boards. Click here to see all tutorials concerning the Mender - Nvidia Jetson integrations on Mender Hub.

Everything You Need to Know about Jetson Orin Nano

Everything You Need to Know about Jetson Orin Nano

Prologue

Nvidia's range of Jetson boards will never be the typical Raspberry Pi alternative for makers. Aimed at entry-level AI-based robots, drones and cameras, the latest board, the $499 Jetson Orin Nano, boosts processing power while keeping the kit compact.

The Jetson Orin Nano 8GB module adds 1024 ampere-based CUDA cores to the 128 CUDA cores of the Nvidia Maxwell GPU. The additional cores and updated architecture mean that the Orin Nano delivers 80 times the AI performance of the Jetson Nano. The Jeston Orin Nano series starts with six Arm A78AE CPU cores, delivering nearly seven times the performance of the Jetson Nano. The Orin Nano uses the same AI architecture as the Jetson AGX Orin module but at a more affordable price.

 

What is Jetson Orin Nano?

Jetson Nano Orin is a strong product line in Nvidia's Jetson family of small, powerful computers designed to power entry-level edge AI applications and devices.

We have seen the most powerful SoC on an embedded system in the world, offering unparalleled performance for the price, thanks to Jetson Orin Nano’s 6-core Arm Cortex-A78AE CPU and a serious Ampere-based GPU setup that offers 1028 CUDA cores and 32 third-generation Tensor cores, making it computationally the most powerful SoC we've seen.

If you noticed that the “AE” suffix was up there, it refers to a special core architecture that can accomplish SIL 3 safety for essential industrial applications with unique smodes. It is interesting to note that Tegra's Orin series chips are reportedly the first to license this core – the Orin Nano is truly at the cutting edge of technology.

On board is 8GB of LPDDR5 RAM, shared by the CPU and GPU, offering an average memory bandwidth of 68GB/s. Although it isn't as fast as GDDR5 or HBM2 RAM used in discrete GPUs, it shouldn't cause any bottlenecking.

 

 

Hardware

The Jetson Orin Nano SOM is available in 4GB and 8GB versions, the latter offering twice the memory and GPU performance. The Developer Kit only includes the 8GB version. The compact carrier board features DisplayPort, USB, Gigabit Ethernet and a USB Type-C port. It also has two MIPI CSI connectors, a 40-pin GPIO header, a PWM header, and an external button control header. The board has expansion options, including a microSD slot, an M.2 Key E slot for Wi-Fi, and two M.2 Key M slots for PCIe. The CPU is a 6-core Arm Cortex-A78AE and the GPU is an NVIDIA Ampere with 1,024 CUDA cores and 32 Tensor cores. The board has no accelerators and supports software-only H.264 video encoding up to 3x 1080p30 and decoding up to 1x 4k60, 2x 4k30, 5x 1080p60 or 11x 1080p30. The board measures approximately 100x79x21mm.

 

Software

The Jetson Orin Nano runs JetPack, NVIDIA's embedded software stack based on Ubuntu Linux. It was tested on a pre-release version of JetPack 5.1.1, which has minor bugs that should be fixed soon. JetPack 5.1.1 is based on Ubuntu 20.04.5 LTS and comes with various software, but it doesn't include a way to train networks on the device. Therefore, training must be done off-device, which limits its potential as an affordable all-in-one workstation for AI development. However, the Jetson Orin Nano is supported by an impressive software stack, including the TAO Toolkit for training and the Omniverse Replicator for synthetic dataset generation. It supports two M.2 Key M PCIe slots for high-capacity storage and a pre-configured M.2 Key E slot for Wi-Fi connectivity.

 

The performance

The Jetson Orin Nano offers 80x the AI performance of the older Jetson Nano, but this is based on INT8 precision on the new device and FP16 on the old. When using FP32 precision on both devices, the gain drops to 5.4x, which is still impressive. In addition to switching to a new GPU architecture, the Jetson Orin Nano has eight times as many CUDA cores as the Jetson Nano, plus 32 Tensor cores as well. Two additional cores are running at marginally faster clock speeds, and there is double the memory with the move to LPDDR5 which offers twice as much bandwidth. In addition, the processor has moved to a newer Arm Cortex architecture, which offers two and a half times the bandwidth of the Jetson Nano. However, there are some sacrifices, including the lack of NVDLAs, PVAs, and hardware video encoders. The focus is on on-device edge AI, and the Jetson Orin Nano performs significantly better than the Jetson Nano on various networks. The Jetson Orin Nano is efficient and configurable in 15W full-performance and 7W reduced-power modes.

 

 

Jetson Orin Nano VS Jetson Nano

It is important not to compare the two Nanos directly, as they serve vastly different markets, and are only similar in name. NVIDIA's $149 Jetson Nano remains an entry-level artificial intelligence platform designed for education and makers, while the Jetson Orin Nano is much higher performance than the older, higher-end Jetson TX2 and Xavier NX-based systems designed for more serious projects and industrial deployments.

There are 4 Cortex A57 cores clocked at 1.47 GHz on the original Jetson Nano, along with 128 Maxwell GPU cores and 4GB of LPDDR4 RAM. It has a lower power rating than its newer counterpart. It is clear that edge technology has advanced a lot in the past few years when you compare the specs side-to-side.

 

Specification comparison

 

 

Jetson Orin Nano

Jetson Nano

CPU

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 architecture with 1024 Nvidia CUDA cores and

Nvidia Maxwell architecture with 128 Nvidia CUDA cores

 

32 Tensor cores

 

Memory

8GB 128-bit LPDDR5

4 GB 64-bit LPDDR4, 1600MHz 25.6 GB/s

 

68 GB/s

 

Storage

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Micro SD

16 GB eMMC 5.1

 

NVMe M.2 via Carrier Board

Micro SD

Power

7W to 15W (5V Input Voltage)

20W (Max 5V at 4 Amps)

Dimensions

69 x 45 x 21 mm

69.6 x 45 x 20 mm

Camera

2x MIPI CSI-2 22-pin Camera Connectors

12 lanes (3x4 or 4x2) MIPI CSI-2 D-PHY 1.1

M.2 Key M

x4 PCIe Gen 3

 

 

X2 PCIe Gen 3

 

M.2 Key E

PCIe (x1), USB 2.0, UART, I2S, and I2C

1 x M.2 Key E

USB

4 x USB 3.2 Gen2

4x USB 3.0

 

1 x Type C for debug and device mode

1 x USB 2.0 Micro-B

Networking

Gigabit Ethernet

Gigabit Ethernet

 

RTL8822CE 802.11ac PCIe Wireless Network Adapter

 

Display

DisplayPort 1.2

HDMI 2.0 and eDP 1.4

GPIO

40 Pin GPIO

40 Pin GPIO

 

12 Pin Button Header

 

 

4 Pin Fan Header

 

Power

DC 9-19V Barrel Jack

DC Barrel Jack 20W (Max 5V at 4 Amps)

Dimensions

100 x 79 x 21 mm (Height includes Orin Nano module and cooling solution)

100 x 80 x 29mm (Height includes Jetson Nano module and cooling solution)

 

 

In passing, there is no visible difference between the Orin Nano and the Jetson Nano, except that the Orin Nano has a fan built into the heatsink and there is no HDMI port, instead, there is a USB-C port which replaces the Jetson Nano's micro USB port. Even when running at the full 15W, the fan remained quiet, unlike other SBC fans which have been tested before. The fan remained silent even when testers ran one of Nvidia's recommended inference benchmarks.

 

 

Final Words

The new Jetson Orin Nano has impressed reviewers with its powerful hardware and software capabilities. It is one of the fastest and graphically most powerful single-board computers tested, and NVIDIA's support and tools make AI development fast and easy. Although it lacks some hardware features, this does not take away from the impressive performance achievable from such a small device. While it is more expensive than most SBCs at $499, it offers more features and capabilities. For beginners in AI, the $149 Jetson Nano may be a better option, but for those experienced in the field, the Jetson Orin Nano is a must-have for tackling more demanding projects.

Whether you are a maker looking for a powerful SBC for AI or the classic Raspberry Pi product, Elecrow provides you with the necessary electronic components for your DIY electronic projects with these powerful embedded SBCs, and Elecrow can even help you turn your DIY electronic ideas into reality and mass produce them for commercial purposes and make your own profits! Join Elecrow's Partner Seller Program for more details.

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