Jetson
The Jetson line of embedded Linux AI and computer vision compute modules and devkits from NVIDIA:
- Jetson TK1: single-board 5" x 5" computer featuring Tegra K1 SOC (quad-core 32-bit Cortex-A15 + 192-core Kepler GPU), 2GB DDR3, and 8GB eMMC.
- Jetson TX1: carrier-board + compute module featuring Tegra X1 SOC (quad-core 64-bit Cortex-A57 + 256-core Maxwell GPU), 4GB 64-bit LPDDR4, and 16GB eMMC.
- Jetson TX2: carrier-board + compute module featuring Tegra X2 SOC (quad-core 64-bit Cortex-A57 + dual-core NVIDIA Denver2 CPU + 256-core Pascal GPU), 8GB 128-bit LPPDR4, 32GB eMMC.
- Jetson Nano: carrier-board + compute module featuring Tegra X1 SOC (quad-core 64-bit Cortex-A57 + 128-core Maxwell GPU), 4GB 64-bit LPDDR4, 4K video encoder/decoder.
- Jetson Xavier NX: compute module featuring Xavier SOC (6-core 64-bit ARMv8.2 + 384-core Volta GPU with Tensor Cores + dual DLAs), 8GB 128-bit LPDDR4x, 16GB eMMC.
- Jetson AGX Xavier: carrier-board + compute module featuring Xavier SOC (8-core 64-bit ARMv8.2 + 512-core Volta GPU with Tensor Cores + dual DLAs), 32GB 256-bit LPDDR4x, 32GB eMMC.
Here are some quick links and references to get started:
- Jetson Site - developer.nvidia.com/embedded
- Jetson Zoo - eLinux.org/Jetson_Zoo
- JetPack Downloads - developer.nvidia.com/jetpack
- Community Forums - forums.developer.nvidia.com
Contents
NVIDIA Jetson Modules
| Features | Jetson Nano | Jetson TX1 | Jetson TX2 series | Jetson Xavier NX | Jetson AGX Xavier series |
|---|---|---|---|---|---|
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| 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 | 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 Gen 3 x5 | 1x4 + 1x1 | 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 JetPack supports all of the Jetson's and includes the OS, BSP, drivers, tools, and SDKs like CUDA.
JetPack Components
- Linux4Tegra (L4T)
- Ubuntu 18.04 aarch64
- 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.
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.
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, and custom design services.
Each Jetson wiki page includes a list of ecosystem products that are compatible with it:
