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The '''[[Jetson Zoo]]''' is a repository of open-source frameworks and packages that can be installed on Jetson, in addition pre-trained DNN models. It provides instructions and pre-built binary installers for popular Machine Learning frameworks such as TensorFlow and PyTorch. | The '''[[Jetson Zoo]]''' is a repository of open-source frameworks and packages that can be installed on Jetson, in addition pre-trained DNN models. It provides instructions and pre-built binary installers for popular Machine Learning frameworks such as TensorFlow and PyTorch. | ||
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== Ecosystem Products & Cameras == | == Ecosystem Products & Cameras == | ||
Revision as of 07:59, 18 May 2021
The NVIDIA Jetson line of embedded Linux AI and computer vision compute modules and devkits:
- Jetson AGX Xavier Developer Kit and series of production modules
- Jetson Xavier NX Developer Kit and production module
- Jetson Nano Developer Kit, Jetson Nano 2GB Developer Kit, and production module
- Jetson TX2 Developer Kit and series of production modules
- Jetson TX1 Developer Kit and production module
- Jetson TK1 Developer Kit for the Tegra K1 SOC
Here are some quick links and references to get started:
- Jetson Developer Site - developer.nvidia.com/embedded
- Jetson Zoo - eLinux.org/Jetson_Zoo
- JetPack Downloads - developer.nvidia.com/jetpack
- Community Forums - forums.developer.nvidia.com
- Partner Products - Supported Cameras | Carrier Boards and Production Systems
Contents
NVIDIA Jetson Modules
| Features | Jetson Nano | Jetson TX1 | Jetson TX2 series | Jetson Xavier NX | Jetson AGX Xavier series |
|---|---|---|---|---|---|
 |
 |
 |
 |
 | |
| CPU | ARM Cortex-A57 (quad-core) @ 1.43GHz | ARM Cortex-A57 (quad-core) @ 1.73GHz | ARM Cortex-A57 (quad-core) @ 2GHz +
NVIDIA Denver2 (dual-core) @ 2GHz |
NVIDIA Carmel ARMv8.2 (6-core) @ 1.4GHz
(6MB L2 + 4MB L3) |
NVIDIA Carmel ARMv8.2 (8-core) @ 2.26GHz
(4x2MB L2 + 4MB L3) |
| GPU | 128-core NVIDIA Maxwell @ 921MHz | 256-core NVIDIA Maxwell @ 998MHz | 256-core NVIDIA Pascal @ 1300MHz | 384-core Volta @ 1100MHz + 48 Tensor Cores | 512-core Volta @ 1377 MHz + 64 Tensor Cores |
| DL | NVIDIA GPU support (CUDA, cuDNN, TensorRT) | dual NVIDIA Deep Learning Accelerators | |||
| Memory | 4GB 64-bit LPDDR4 @ 1600MHz | 25.6 GB/s | 8GB 128-bit LPDDR4 @ 1866Mhz | 58.3 GB/s | 8GB 128-bit LPDDR4x @ 1600MHz | 51.2GB/s | 32GB 256-bit LPDDR4x @ 2133MHz | 137GB/s | |
| Storage | MicroSD card | 16GB eMMC 5.1 | 32GB eMMC 5.1 | 16GB eMMC 5.1 | 32GB eMMC 5.1 |
| Vision | NVIDIA GPU support (CUDA, VisionWorks, OpenCV) | 7-way VLIW Vision Accelerator | |||
| Encoder | 4Kp30, (2x) 1080p60, (4x) 1080p30 | 4Kp60, (3x) 4Kp30, (4x) 1080p60, (8x) 1080p30 | (2x) 4Kp30, (6x) 1080p60, (12x) 1080p30 | (4x) 4Kp60, (8x) 4Kp30, (32x) 1080p30 | |
| Decoder | 4Kp60, (2x) 4Kp30, (4x) 1080p60, (8x) 1080p30 | (2x) 4Kp60, (4x) 4Kp30, (7x) 1080p60 | (2x) 4Kp60, (4x) 4Kp30, (12x) 1080p60 | (2x) 8Kp30, (6x) 4Kp60, (12x) 4Kp30 | |
| Camera | 12 lanes MIPI CSI-2 | 1.5 Gbps per lane | 12 lanes MIPI CSI-2 | 2.5 Gbps per lane | 14 lanes MIPI CSI-2 | 2.5 Gbps per lane | 16 lanes MIPI CSI-2 | 6.8125Gbps per lane | |
| Display | 2x HDMI 2.0 / DP 1.2 / eDP 1.2 | 2x MIPI DSI | (2x) DP 1.4 / eDP 1.4 / HDMI 2.0 @ 4Kp60 | (3x) eDP 1.4 / DP 1.2 / HDMI 2.0 @ 4Kp60 | ||
| Wireless | M.2 Key-E site on carrier | 802.11a/b/g/n/ac 2×2 867Mbps | Bluetooth 4.0 | 802.11a/b/g/n/ac 2×2 867Mbps | Bluetooth 4.1 | M.2 Key-E site on carrier | |
| Ethernet | 10/100/1000 BASE-T Ethernet | ||||
| USB | (4x) USB 3.0 + Micro-USB 2.0 | USB 3.0 + USB 2.0 | USB 3.1 + (3x) USB 2.0 | (3x) USB 3.1 + (4x) USB 2.0 | |
| PCIe | PCIe Gen 2 x1/x2/x4 | PCIe Gen 2 x5 | 1×4 + 1x1 | PCIe Gen 2 x5 | 1×4 + 1×1 or 2×1 + 1×2 | PCIe x5 | 1x4 (Gen 4) + 1x1 (Gen 3) | PCIe Gen 4 x16 | 1x8 + 1x4 + 1x2 + 2x1 |
| CAN | Not Supported | Dual CAN bus controller | Single CAN bus controller | Dual CAN bus controller | |
| Misc IO | UART, SPI, I2C, I2S, GPIOs | ||||
| Socket | 260-pin edge connector, 45x70mm | 400-pin board-to-board connector, 50x87mm | 260-pin edge connector, 45x70mm | 699-pin board-to-board connector, 100x87mm | |
| Thermals | -25°C to 80°C | ||||
| Power | 5/10W | 10W | 7.5W | 10/15W | 10/15/30W |
| Perf | 472 GFLOPS | 1 TFLOPS | 1.3 TFLOPS | 21 TeraOPS | 32 TeraOPS |
Software Support
NVIDIA Jetson production modules and developer kits are all supported by the same NVIDIA software stack, enabling you to develop once and deploy everywhere. JetPack SDK includes the latest Jetson Linux Driver Package (L4T) with Linux operating system and CUDA-X accelerated libraries and APIs for AI Edge application development. It also includes samples, documentation, and developer tools for both host computer and developer kit, and supports higher level SDKs such as DeepStream for streaming video analytics and Isaac for robotics.
JetPack Components
- NVIDIA Jetson Linux (L4T)
- CUDA Toolkit
- cuDNN
- TensorRT
- VisionWorks
- DeepStream
- OpenCV
- OpenGL
- Vulkan
- V4L2 extensions
- GStreamer extensions
- L4T Multimedia API
- NVIDIA Nsight Systems
- NVIDIA Nsight Graphics
- NVIDIA Nsight Compute
See docs.nvidia.com/jetson for online documentation about JetPack.
See developer.nvidia.com/jetpack to download the latest JetPack.
See these
Jetson Zoo
The Jetson Zoo is a repository of open-source frameworks and packages that can be installed on Jetson, in addition pre-trained DNN models. It provides instructions and pre-built binary installers for popular Machine Learning frameworks such as TensorFlow and PyTorch.
Jetson Filesystem Emulation
Ecosystem Products & Cameras
The Jetson Ecosystem includes a diverse set of companies producing add-ons, accessories, sensors, and software for Jetson such as carrier boards, enclosures, cameras, production systems, and custom design services.
For more info, see the directory of Supported Cameras and Carrier Boards and Production Systems.
For those interested in real-time Linux support, see Concurrent RedHawk.
Also, each Jetson wiki page includes a list of ecosystem products that are compatible with it:
