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  • [Jetson TX2] NVIDIA Jetson TX2 参数介绍

    千次阅读 2020-05-28 21:21:50
    目录0 参数一览1 Jetson TX2 Overview2 Hardware2.1 Jetson TX2 vs Jetson TX13 Sippy or Speedy4 Software5 Initial Impressions6 Conclusion7 Pictures, Natch!8 Appendix8.1 Tegra(图睿)8.2 Jetson TX2 GPU8.3 ...
  • 本文在原帖:https://blog.csdn.net/Gerwels_JI/article/details/84576935#t4的基础上作了增补完善,非笔者原创,特此声明。

    0 参数一览

    1 Jetson TX2 Overview

    The Jetson TX2 is a new iteration of the Jetson Development Kit which doubles the computing power and power efficiency of the earlier Jetson TX1.

    The Jetson TX1 Dev Kit introduced a new module format, where a standardized Tegra Module is plugged into a carrier board(载板). While the Jetson TX2 uses the same carrier board as the Jetson TX1, the actual Tegra TX2 Module itself is all new.

    2 Hardware

    • 包括1块GPU + 2块CPU:
      The Jetson TX2 features a NVIDIA Pascal GPU with 256 CUDA capable cores. The CPU complex consists of two ARM v8 64-bit CPU clusters which are connected by a high-performance coherent interconnect fabric(通畅的互联结构). The Denver 2 (Dual-Core) CPU cluster is optimized for higher single-thread(单线程) performance; the second CPU cluster is an ARM Cortex-A57 QuadCore which is better suited for multi-threaded(多线程) applications.
    • 拥有128-bit memory controller + 8 GB RAM and 32 GB ROM
      The memory subsystem incorporates a 128-bit memory controller(128位内存控制器), which provides high bandwidth(32Gbps) LPDDR4** support. 8 GB LPDDR4 Main Memory and 32 GB eMMC Flash memory are integrated on the Module. Going to a 128-bit design from the TX1 64-bit is a major performance enhancement.
    • 支持音视频硬件编解码
      The Module also supports hardware video encoders and decoders which support 4K ultra-high-definition video at 60 fps in several different formats(格式). This is slightly different than the hybrid Jetson TX1 module, which used both dedicated hardware and software which was running on the Tegra SoC for those tasks. Also included is an Audio Processing Engine with full hardware support for multi-channel audio.
    • 包括Wi-Fi and Bluetooth模块
      The Jetson TX2 supports Wi-Fi and Bluetooth wireless connectivity. Wi-fi is much improved over the earlier Jetson TX1. Gigabit Ethernet BASE-T is included. Here’s a comparison between the TX1 and the TX2.

    2.1 Jetson TX2 vs Jetson TX1

    The carrier board, which is common between both the Jetson TX2 and the Jetson TX1, has the following I/O connectors:

    • USB 3.0 Type A
    • USB 2.0 Micro AB (supports recovery and host mode)
    • HDMI
    • M.2 Key E
    • PCI-E x4
    • Gigabit Ethernet
    • Full size SD card reader
    • SATA data+power
    • Display expansion header
    • Camera expansion header

    There are two expansion headers(两个拓展头), a 40 pin, 2.54mm spaced header with signals laid out similarly to the Raspberry Pi, and a 30 pin, 2.54mm spaced header for extra GPIO.

    The Jetson also includes a 5MP camera in the camera expansion header, and a display expansion header for adding extra display panels.

    The Jetson TX2 has added a CAN bus controller to the module. CAN is a network format that is frequently used in automobiles and other vehicles. The CAN bus signals are available directly on the GPIO Expansion Header.
    在这里插入图片描述

    3 Sippy or Speedy

    • Jetson TX2 Dual Operating Modes: Max-Q & Max-P
      This new generation brings a configurable(可配置的) amount of performance increase depending on power consumption requirements. NVIDIA has engineered two modes:
    1. Max-Q is the name of the energy efficiency mode which clocks the Parker SoC for efficiency over performance and draws about 7.5W, right before the bend in the power/performance curve. The result of this mode is that the TX2 has similar performance to a TX1 in max performance mode, while drawing about half the power!
    2. In Max-P mode, the TX2 just flat out goes for it in the power budget of 15W. This provides about twice the performance of the Jetson TX1 at its maximum clock rate.
      3.

    4 Software

    -预装ubuntu16.04,并提供JetPack 3.0
    There are several changes to the Jetson TX2 software stack(软件栈). The Jetson TX2 runs a Developer Preview of an Ubuntu 16.04 variant named L4T 27.1. The Linux Kernel is 4.4, a newer version than the earlier Jetson TX1 version 3.10. There have been changes to the boot flow, with additional firmware managers added to the mix. The Jetson TX2 comes with a long list of software libraries, and a good selection of samples with source code.
    The new JetPack 3.0 installer is available to flash and copy system software to the Jetson TX2.

    5 Initial Impressions

    NVIDIA claims that the Jetson TX2 is twice as fast as the Jetson TX1. After booting the machine, this surely seems the case. The entire experience feels very much like a desktop/laptop level machine. Doubling the memory (and the memory bus speed) surely helps with that feeling. Previous Jetsons experience quite a bit of memory pressure when running memory intensive, desktop applications like web browsers. The TX2 doesn’t even notice.

    Running a handful of compiles and tests on applications like Caffe proved that the Jetson TX2 is indeed quite a bit faster than the earlier Jetson TX1 (see the video for one of the tests).

    One of the fun samples that comes with the Jetson TX2 is an object recognition example which is demonstrated in the video. The deep learning sample uses Caffe along with ImageNet and uses the onboard camera to grab imagery.

    Note that we haven’t performed any performance tuning for the demos, this is how it runs fresh out the box!

    If you want some hardcore numbers, go over to Phoronix and check out NVIDIA Jetson TX2 Linux Benchmarks(推荐阅读6).

    6 Conclusion

    Stay tuned as we begin working with the TX2 to better understand how to take advantage of the extra performance. Find out more on the NVIDIA Developers site.#

    7 Pictures, Natch!

    整体图
    TX2 panel layout
    ["TX2的心脏"]
    摄像头模块

    8 Appendix

    8.1 Tegra(图睿)

    Tegra是一种采用单片机系统设计片上系统(SoC, system-on-a-chip)芯片,它集成了ARM架构处理器和NVIDIA的Geforce GPU,并内置了其它功能,产品主要面向小型设备。和Intel以PC为起点的x86架构相比,ARM架构的Tegra更像是以手机处理器为起点做出的发展。
    注意:它不能运行x86 PC上的Windows XP等操作系统,但在手机上应用多年的ARM架构轻量级操作系统更能适应它高速低功耗的需求。

    8.2 Jetson TX2 GPU

    256-core NVIDIA Pascal @ 1300MHz

    8.3 Jetson TX2 CPU

    ARM Cortex-A57 (quad-core) @ 2GHz +
    NVIDIA Denver2 (dual-core) @ 2GHz

    8.4 Jetson TX2 memory

    8GB 128-bit LPDDR4 @ 1866Mhz | 58.3 GB/s
    (LPDDR4: Low Power Double Data Rate 4)

    8.4.1 介绍

    LPDDR可以说是全球范围内最广泛使用于移动设备的“工作记忆”内存。全新的20nm 8Gb LPDDR4内存,在性能和集成度上都比20纳米级4Gb LPDDR3内存提高一倍。 [1]
    LPDDR4可提供32Gbps的带宽,为DDR3 RAM的2倍。当前,Galaxy S5、Note 4和Nexus6均采用DDR3标准。更快速的RAM意味着应用的启动速度更快,这对于在执行多任务时启动重量级应用至关重要

    8.4.2 性能

    由于I/O接口数据传输速度最高可达3200Mbps,是通常使用的DDR3 DRAM的两倍,新推出的8Gb LPDDR4内存可以支持超高清影像的拍摄和播放,并能持续拍摄2000万像素的高清照片。
    与LPDDR3内存芯片相比,LPDDR4的运行电压降为1.1伏,堪称适用于大屏幕智能手机和平板电脑、高性能网络系统的最低功耗存储解决方案。以2GB内存封装为例,比起基于4Gb LPDDR3芯片的2GB内存封装,基于8Gb LPDDR4芯片的2GB内存封装因运行电压的降低和处理速度的提升,最大可节省40%的耗电量。同时,新产品的输入/输出信号传输采用三星独有的低电压摆幅终端逻辑(LVSTL, Low Voltage Swing Terminated Logic),不仅进一步降低了LPDDR4芯片的耗电量,并使芯片能在低电压下进行高频率运转,实现了电源使用效率的最优化。

    8.4.3 DDR3和DDR4的区别

    这两个玩穿了的内容,就不再阐述了,我找了一篇CSDN,在文末的推荐阅读3里面,不懂的建议看看,总体来说就是两者的频率和兼容主板都是不同的。所以总之,DDR4 和 DDR3 内存是无法通用的,两者不兼容。不过目前市场有 DDR4 和 DDR3 内存都支持的 100 系列主板,这种非主流主板需求量比较小,不太建议。既然配了100、200系列的主板,就直接使用DDR4就行了。

    8.5 eMMC & SSD

    • eMMC和SSD主要是满足不同需求而发展出来的NAND应用
    • eMMC
      平板和手机为了满足移动性的需求,所以需要做到轻,薄;尤其是功耗要很低,因此eMMC就诞生了;所以eMMC接口是用IO pin来定义的,这样接口简单,功耗低;另外eMMC对于苹果iPad、安卓平板电脑、手机的作用也是巨大的,平板和手机都比较小,所以eMMC是把控制器和NAND颗粒封装在一个package里面,这也造成eMMC不能放很多NAND颗粒,容量比较低。总结eMMC特点就是功耗低,容量小,随机读写性能差;
    • SSD
      SSD主要是为了满足大容量存储尤其是数据中心等应用场合,SSD成PC电脑的性能催化剂,读写性能尤其是随机读写性能快。为可达到这样的性能,SSD控制器都是使用高速总线,刚开始是SATA,现在PCIE也越来越多,以后可能会用光纤;NAND颗粒都有多个通道用于提升容量和读写性能。所以SSD功耗也很大。总结SSD特点就是功耗大, 容量大,读写快。
    • 推荐阅读4

    8.6 CAN & bus controller

    9 推荐阅读

    1. 在终端人工智能领域,NVIDIA的Jetson TX2为何如此受青睐?
      https://www.leiphone.com/news/201704/TEpCLJfL6ni05AJZ.html
    2. NVIDIA Jetson TX2 Development Kit
      https://www.jetsonhacks.com/2017/03/14/nvidia-jetson-tx2-development-kit/
    3. DDR扫盲——DDR与DDR2、DDR3的区别
      https://blog.csdn.net/chenzhen1080/article/details/82951214?utm_source=blogxgwz7
    4. emmc和ssd的区别
      https://blog.csdn.net/hawk_lexiang/article/details/78228789
    5. 软件堆栈和硬件堆栈概念分析
      http://www.elecfans.com/emb/587830.html
    6. NVIDIA Jetson TX2 Linux Benchmarks
      https://www.phoronix.com/scan.php?page=article&item=jetson-tegra-x2&num=1
    7. 官网embedded computing
      https://developer.nvidia.com/embedded-computing
  • 展开全文
  • Jetson TX2

    2021-01-12 07:51:44
    i have a trouble with your realtime detection on my Jetson TX2. Because my TX2 just have ability to install CUDA library 9.0, and CUDNN have a lower version than your project. May be can you recommand...
  • Jetson TX2、原装数据线和电源线(电源公头需要自备)、搭载Ubuntu 64位系统的PC(仅支持Ubuntu14.04/16.04,我找了一台18.04的机器也是各种报错,报错内容就是18.04版本不是官方支持的版本,所以建议直接用推荐的...

    声明:本文非笔者原创,转载自https://www.jianshu.com/p/bb4587014349,仅供学习参考使用

    1 准备工作

    • 必备材料:
      Jetson TX2、原装数据线和电源线(电源公头需要自备)、搭载Ubuntu 64位系统的PC(仅支持Ubuntu14.04/16.04,我找了一台18.04的机器也是各种报错,报错内容就是18.04版本不是官方支持的版本,所以建议直接用推荐的版本)、路由器(强烈建议,后面会介绍)、三根网线、各种线。
    • 注意:
      笔者使用的是VMware+Ubuntu 16.04的虚拟机环境成功对Jetson TX2刷机到Jetpack 3.3
      在这里插入图片描述
      可以看到,官网推荐使用14.04版本的linux系统刷Jetpack的工具包:在这里插入图片描述
    • 刷机软件和相关的数据包,Jetpack、CUDA、cuDNN、OPENCV、OpenGL、Nsight、TensorRT等。(其实你直接选择all即可,以免后期又要重新配置jetpack里的各种工具,实在是麻烦,大佬随意

    2 注意事项

    1. 准备好主机和TX2,连接网络,插上路由器(外网进一根线,两根线分别接主机和TX2),从官网下载最新的Jetpack 3.3
    2. JetPack是一个x86二进制文件,不能在基于ARM的机器上运行,需要通过宿主机烧录到ARM构架的TX2中。
    3. 运行时注意路径问题,在相同路径下进行JetPack操作
    4. 如果是使用虚拟机作为host PC,请保证虚拟机硬盘在40G以上,且虚拟机的网络选择桥接。(笔者使用的以往的虚拟机,直接分配了100GB的内存)

    3 刷机步骤

    1. 在虚拟机(主机)上运行JetPack
    2. 选择Jetson TX2开发板
    3. 选择Jetpack刷机包(不断点击next,有问题解决问题,差环境配置环境)
    4. 在开发板上一顿操作然后在Post Installation上按下Enter键就可以等待安装了(等待下载,下载时间由本地网速决定)

    4 详细步骤

    由于网上太多关于Jetson TX2开发板刷机的文章,我这里不再整理阐述,建议先阅读参考2、3、4,最后按照官网提供的How to Install JetPack文档进行安装。

    1. 官网的是最值得参考的:
      How to Install JetPack
    2. 主要参考(讲的很详细,这个人的简书号可以关注一下):
      这是大佬关于本文的教程:https://www.jianshu.com/p/bb4587014349
      这是大佬的主页:Website
    3. 其他参考
      # 1 # Jetson TX2配置--简书
      # 2 # Jetson TX2刷机--CSDN
      # 3 # TX2从入门到放弃学习笔记(1)基础--CSDN

    4 可能遇到的问题

    坑#1 虚拟机网络不好

    虚拟机网络不好 --> 解决办法:VM虚拟机桥接模式无法联网解决办法
    除了Jetson TX2之外,您还需要另一台带有Intel或AMD x86处理器的台式机或笔记本电脑。(我是win 10 专业版系统,安装VMware workstation 12 Pro 虚拟机,并在虚拟机上安装Ubuntu -14.04 64 位操作系统,虚拟机一定要将网络设置为桥接模式,复制物理地址)

    坑#2 Error: non_EN locale

    因为是以前用过的ubuntu,为了方便换成中文版的系统了,Jetpack不能在中文环境(non_EN locale)下进行installation,所以我们需要在ubuntu的设置中将整个系统的语言支持修改成美国(EN)状态。

    坑#3 Error: Please run JetPack as a non-elevated user.

    解决办法:普通权限运行Jetpack,不用sudo或者root用户,直接运行

    ./{Jetpack}
    
    • 1

    坑#4 ERROR:return code:127 /bin/bash: xterm: command not found

    apt install xterm
    
    • 1

    坑#7 桥接模式的设置

    点击虚拟机–>设置,将网络适配器中的网络连接改为桥接模式,并勾选复制物理网络连接状态(我相信这也是大多数人所遇到的坑)
    在这里插入图片描述
    如果修改之后桥接模式不能联网,那就试试下面方法
    VM虚拟机桥接模式无法联网解决办法
    然后再重新执行一遍就好了!

    坑#6

    • 一定要买个USBhub插tx2上,不然刷机很不方便,需要接鼠标键盘甚至U盘
    • 连接主机和tx2到一起时一定要用网线,不要指望板子的无线,可能会出点小问题。

    5 Demo

    参考文章:https://www.cnblogs.com/Mufasa/p/8414376.html

    5.1 OceanTTF demo

    The CUDA samples directory is copied to the home directory on the device by JetPack. The built binaries are in the following directory:

    /home/ubuntu/NVIDIA_CUDA-_Samples/bin/armv7l/linux/release/gnueabihf/

    这里的version需要看你自己安装的CUDA版本而定

    Run the samples at the command line or by double-clicking on them in the file browser. For example, when you run the oceanFFT sample, the following screen is displayed.
    在这里插入图片描述

    5.2 车辆检测demo

    nvidia@tegra-ubuntu:~$ cd ~/tegra_multimedia_api/samples/backend/
    nvidia@tegra-ubuntu:~/tegra_multimedia_api/samples/backend$ ./backend 1 ../../data/Video/sample_outdoor_car_1080p_10fps.h264 H264 --trt-deployfile ../../data/Model/GoogleNet_one_class/GoogleNet_modified_oneClass_halfHD.prototxt --trt-modelfile ../../data/Model/GoogleNet_one_class/GoogleNet_modified_oneClass_halfHD.caffemodel --trt-forcefp32 0 --trt-proc-interval 1 -fps 10
    Net has batch_size, channel, net_height, net_width:1 3 540 960
    forced_fp32 has been set to 0(using fp16)i/samples/backend$ ./backend 1 ../../data/Video/sample_outdoor_car_1080p_10fps.h264 H264 --trt-deployfile ../../data/Model/GoogleNet_one_class/GoogleNet_modified_ooutputs coverage
    outputs bboxes
    Create TRT model cache
    outputDim c 1 w 240 h 132
    outputDimsBBOX.c() 4 w 240 h 132
    Failed to query video capabilities: Inappropriate ioctl for device
    NvMMLiteOpen : Block : BlockType = 261 
    TVMR: NvMMLiteTVMRDecBlockOpen: 7647: NvMMLiteBlockOpen 
    NvMMLiteBlockCreate : Block : BlockType = 261 
    Failed to query video capabilities: Inappropriate ioctl for device
    Failed to query video capabilities: Inappropriate ioctl for device
    Starting decoder capture loop thread
    TVMR: cbBeginSequence: 1179: BeginSequence  1920x1088, bVPR = 0
    TVMR: LowCorner Frequency = 0 
    TVMR: cbBeginSequence: 1529: DecodeBuffers = 5, pnvsi->eCodec = 4, codec = 0 
    TVMR: cbBeginSequence: 1600: Display Resolution : (1920x1080) 
    TVMR: cbBeginSequence: 1601: Display Aspect Ratio : (1920x1080) 
    TVMR: cbBeginSequence: 1669: ColorFormat : 5 
    TVMR: cbBeginSequence:1683 ColorSpace = NvColorSpace_YCbCr601
    TVMR: cbBeginSequence: 1809: SurfaceLayout = 3
    TVMR: cbBeginSequence: 1902: NumOfSurfaces = 12, InteraceStream = 0, InterlaceEnabled = 0, bSecure = 0, MVC = 0 Semiplanar = 1, bReinit = 1, BitDepthForSurface = 8 LumaBitDepth = 8, ChromaBitDepth = 8, ChromaFormat = 5
    TVMR: cbBeginSequence: 1904: BeginSequence  ColorPrimaries = 2, TransferCharacteristics = 2, MatrixCoefficients = 2
    [INFO] (NvEglRenderer.cpp:109) <renderer0> Setting Screen width 1920 height 1080
    libv4l2_nvvidconv (0):(792) (INFO) : Allocating (17) OUTPUT PLANE BUFFERS Layout=1
    libv4l2_nvvidconv (0):(808) (INFO) : Allocating (17) CAPTURE PLANE BUFFERS Layout=0
    libv4l2_nvvidconv (0):(792) (INFO) : Allocating (17) OUTPUT PLANE BUFFERS Layout=0
    libv4l2_nvvidconv (0):(808) (INFO) : Allocating (17) CAPTURE PLANE BUFFERS Layout=0
    Query and set capture  successful
    Time elapsed:60 ms per frame in past 100 frames
    TVMR: FrameRate = 10.000000 
    Time elapsed:62 ms per frame in past 100 frames
    TVMR: FrameRate = 10.000000 
    Time elapsed:62 ms per frame in past 100 frames
    TVMR: FrameRate = 10.000000 
    Time elapsed:65 ms per frame in past 100 frames
    TVMR: FrameRate = 10.000000 
    Time elapsed:61 ms per frame in past 100 frames
    Could not read nal unit from file. EOF or file corrupted
    Input file read complete
    TVMR: NvMMLiteTVMRDecDoWork: 6531: NVMMLITE_TVMR: EOS detected
    TVMR: TVMRBufferProcessing: 5486: Processing of EOS 
    TVMR: TVMRBufferProcessing: 5563: Processing of EOS Done
    Exiting decoder capture loop thread
    Time elapsed:64 ms per frame in past 100 frames
    TVMR: TVMRFrameStatusReporting: 6132: Closing TVMR Frame Status Thread -------------
    TVMR: TVMRVPRFloorSizeSettingThread: 5942: Closing TVMRVPRFloorSizeSettingThread -------------
    TVMR: TVMRFrameDelivery: 5982: Closing TVMR Frame Delivery Thread -------------
    TVMR: NvMMLiteTVMRDecBlockClose: 7815: Done 
    App run was successful
    

    在这里插入图片描述

    6 Appendix

    DIY一个亚克力板作为防尘,后期会跑一个目标检测的demo作为实验品。
    在这里插入图片描述

    7 Referencec

    展开全文
  • NVIDIA JETSON - Jetson TX2 / Jetson TX1

    千次阅读 2018-10-09 14:30:55
    NVIDIA JETSON - Jetson TX2 / Jetson TX1 https://www.nvidia.com/zh-cn/autonomous-machines/embedded-systems-dev-kits-modules/ ...1. JETSON TX2 MODULE - Jetson TX2 模块 This is an

    NVIDIA JETSON - Jetson TX2 / Jetson TX1

    https://www.nvidia.com/zh-cn/autonomous-machines/embedded-systems-dev-kits-modules/
    https://www.nvidia.com/en-us/autonomous-machines/embedded-systems-dev-kits-modules/

    1. JETSON TX2 MODULE - Jetson TX2 模块

    This is an AI supercomputer on a module, powered by NVIDIA Pascal™ architecture. Best of all, it packs this performance into a small, power-efficient form factor that’s ideal for intelligent edge devices like robots, drones, smart cameras, and portable medical devices. It supports all the features of the Jetson TX1 module while enabling bigger, more complex deep neural networks.
    它是一台模块化 AI 超级计算机,采用 NVIDIA Pascal™ 架构。更棒的是,它性能强大,但外形小巧,节能高效,非常适合机器人、无人机、智能摄像机和便携医疗设备等智能边缘设备。它支持 Jetson TX1 模块的所有功能,同时可以铸就更大型、更复杂的深度神经网络。

    在这里插入图片描述

    2. JETSON TX1 MODULE - Jetson TX1 模块

    This AI supercomputer features NVIDIA Maxwell™ architecture, 256 NVIDIA CUDA® cores, 64-bit CPUs, and a power-efficient design. Plus, it includes the latest technology for deep learning, computer vision, GPU computing, and graphics-making it ideal for embedded AI computing.
    这款 AI 超级计算机采用 NVIDIA Maxwell™ 架构,具有 256 个 NVIDIA CUDA® 核心、64 位 CPU,并且采用了节能高效的设计。此外,它还采用了深度学习、计算机视觉、GPU 计算和图形方面的新技术,非常适合嵌入式 AI 计算。

    在这里插入图片描述

    3. MODULE TECHNICAL SPECIFICATIONS - 模块技术规格

    在这里插入图片描述

    4. JETSON DEVELOPER KITS

    JETSON 开发者套件

    4.1 JETSON TX2 DEVELOPER KIT - Jetson TX2 开发者套件

    This kit highlights the hardware capabilities and interfaces of the Jetson TX2 board, comes with design guides and documentation, and is pre-flashed with a Linux development environment. It also supports the NVIDIA Jetpack SDK, which includes the BSP, libraries for deep learning, computer vision, GPU computing, multimedia processing, and more.
    此套件凸显了 Jetson TX2 主板的硬件功能和接口,附带有设计指南和文档,并且利用 Linux 开发环境进行了预先闪存处理。同时,它还支持 NVIDIA Jetpack SDK,包括 BSP、深度学习库、计算机视觉、GPU 计算、多媒体处理等。

    在这里插入图片描述

    4.2 JETSON TX1 DEVELOPER KIT - Jetson TX1 开发者套件

    This kit is a full-featured development platform for AI computing designed to get you up and running fast. It comes pre-flashed with a Linux environment, includes support for many common APIs, and is supported by NVIDIA’s complete development tool chain.
    此套件是一款面向 AI 计算的全功能开发平台,其设计能让您轻松上手和快速开发产品。它配备预先安装了 Linux 环境的闪存,支持许多常见的 API,并且获得 NVIDIA 整个开发工具链的支持。

    在这里插入图片描述

    5. DEVELOPER KIT KEY FEATURES - 开发者套件主要特性

    在这里插入图片描述

    Wordbook

    pascal ['pæsk(ə)l]:n. 帕斯卡 (压力的单位)
    maxwell ['mækswel]:n. 麦克斯韦 (磁通量单位)
    High Efficiency Video Coding,HEVC,H.265 and MPEG-H Part 2
    Heterogeneous Multi-Processor Architecture,HMP
    simultaneous multithreading,SMT
    Denver ['denvə]:n. 丹佛
    Mobile DDR (also known as mDDR, Low Power DDR, LPDDR, or LP-DDR) is a type of double data rate synchronous DRAM for mobile computers.
    embedded MMC,eMMC
    Jetson Development Pack,JetPack

    References

    https://www.nvidia.com/zh-cn/autonomous-machines/embedded-systems-dev-kits-modules/
    https://www.nvidia.com/en-us/autonomous-machines/embedded-systems-dev-kits-modules/

    展开全文
  • Jetson TX2 source

    2018-06-22 19:32:26
    Jetson TX2 source,NVIdia guangfangluntai geichudeTX2 yuanma ke chongxinbianyi
  • Jetson TX2原理图

    2019-09-19 16:11:17
    Jetson TX2 原理图,pdf格式,硬件设计、调试等可参考
  • Jetson TX2TX2i System-on-Module Data Sheet.pdf 数据手册
  • 0 参数一览 ...The Jetson TX2 is a new iteration of the Jetson Development Kit which doubles the computing power and power efficiency of the earlier Jetson TX1. The Jetson TX1 Dev Kit i...

    0 参数一览

    1 Jetson TX2 Overview

    The Jetson TX2 is a new iteration of the Jetson Development Kit which doubles the computing power and power efficiency of the earlier Jetson TX1.

    The Jetson TX1 Dev Kit introduced a new module format, where a standardized Tegra Module is plugged into a carrier board(载板). While the Jetson TX2 uses the same carrier board as the Jetson TX1, the actual Tegra TX2 Module itself is all new.

    2 Hardware

    • 包括1块GPU + 2块CPU:
      The Jetson TX2 features a NVIDIA Pascal GPU with 256 CUDA capable cores. The CPU complex consists of two ARM v8 64-bit CPU clusters which are connected by a high-performance coherent interconnect fabric(通畅的互联结构). The Denver 2 (Dual-Core) CPU cluster is optimized for higher single-thread(单线程) performance; the second CPU cluster is an ARM Cortex-A57 QuadCore which is better suited for multi-threaded(多线程) applications.
    • 拥有128-bit memory controller + 8 GB RAM and 32 GB ROM
      The memory subsystem incorporates a 128-bit memory controller(128位内存控制器), which provides high bandwidth(32Gbps) LPDDR4** support. 8 GB LPDDR4 Main Memory and 32 GB eMMC Flash memory are integrated on the Module. Going to a 128-bit design from the TX1 64-bit is a major performance enhancement.
    • 支持音视频硬件编解码
      The Module also supports hardware video encoders and decoders which support 4K ultra-high-definition video at 60 fps in several different formats(格式). This is slightly different than the hybrid Jetson TX1 module, which used both dedicated hardware and software which was running on the Tegra SoC for those tasks. Also included is an Audio Processing Engine with full hardware support for multi-channel audio.
    • 包括Wi-Fi and Bluetooth模块
      The Jetson TX2 supports Wi-Fi and Bluetooth wireless connectivity. Wi-fi is much improved over the earlier Jetson TX1. Gigabit Ethernet BASE-T is included. Here’s a comparison between the TX1 and the TX2.

    2.1 Jetson TX2 vs Jetson TX1

    The carrier board, which is common between both the Jetson TX2 and the Jetson TX1, has the following I/O connectors:

    • USB 3.0 Type A
    • USB 2.0 Micro AB (supports recovery and host mode)
    • HDMI
    • M.2 Key E
    • PCI-E x4
    • Gigabit Ethernet
    • Full size SD card reader
    • SATA data+power
    • Display expansion header
    • Camera expansion header

    There are two expansion headers(两个拓展头), a 40 pin, 2.54mm spaced header with signals laid out similarly to the Raspberry Pi, and a 30 pin, 2.54mm spaced header for extra GPIO.

    The Jetson also includes a 5MP camera in the camera expansion header, and a display expansion header for adding extra display panels.

    The Jetson TX2 has added a CAN bus controller to the module. CAN is a network format that is frequently used in automobiles and other vehicles. The CAN bus signals are available directly on the GPIO Expansion Header.
    在这里插入图片描述

    3 Sippy or Speedy

    • Jetson TX2 Dual Operating Modes: Max-Q & Max-P
      This new generation brings a configurable(可配置的) amount of performance increase depending on power consumption requirements. NVIDIA has engineered two modes:
    1. Max-Q is the name of the energy efficiency mode which clocks the Parker SoC for efficiency over performance and draws about 7.5W, right before the bend in the power/performance curve. The result of this mode is that the TX2 has similar performance to a TX1 in max performance mode, while drawing about half the power!
    2. In Max-P mode, the TX2 just flat out goes for it in the power budget of 15W. This provides about twice the performance of the Jetson TX1 at its maximum clock rate.
      3.

    4 Software

    -预装ubuntu16.04,并提供JetPack 3.0
    There are several changes to the Jetson TX2 software stack(软件栈). The Jetson TX2 runs a Developer Preview of an Ubuntu 16.04 variant named L4T 27.1. The Linux Kernel is 4.4, a newer version than the earlier Jetson TX1 version 3.10. There have been changes to the boot flow, with additional firmware managers added to the mix. The Jetson TX2 comes with a long list of software libraries, and a good selection of samples with source code.
    The new JetPack 3.0 installer is available to flash and copy system software to the Jetson TX2.

    5 Initial Impressions

    NVIDIA claims that the Jetson TX2 is twice as fast as the Jetson TX1. After booting the machine, this surely seems the case. The entire experience feels very much like a desktop/laptop level machine. Doubling the memory (and the memory bus speed) surely helps with that feeling. Previous Jetsons experience quite a bit of memory pressure when running memory intensive, desktop applications like web browsers. The TX2 doesn’t even notice.

    Running a handful of compiles and tests on applications like Caffe proved that the Jetson TX2 is indeed quite a bit faster than the earlier Jetson TX1 (see the video for one of the tests).

    One of the fun samples that comes with the Jetson TX2 is an object recognition example which is demonstrated in the video. The deep learning sample uses Caffe along with ImageNet and uses the onboard camera to grab imagery.

    Note that we haven’t performed any performance tuning for the demos, this is how it runs fresh out the box!

    If you want some hardcore numbers, go over to Phoronix and check out NVIDIA Jetson TX2 Linux Benchmarks(推荐阅读6).

    6 Conclusion

    Stay tuned as we begin working with the TX2 to better understand how to take advantage of the extra performance. Find out more on the NVIDIA Developers site.#

    7 Pictures, Natch!

    整体图
    TX2的心脏
    摄像头模块

    8 Appendix

    8.1 Tegra(图睿)

    Tegra是一种采用单片机系统设计片上系统(SoC, system-on-a-chip)芯片,它集成了ARM架构处理器和NVIDIA的Geforce GPU,并内置了其它功能,产品主要面向小型设备。和Intel以PC为起点的x86架构相比,ARM架构的Tegra更像是以手机处理器为起点做出的发展。
    注意:它不能运行x86 PC上的Windows XP等操作系统,但在手机上应用多年的ARM架构轻量级操作系统更能适应它高速低功耗的需求。

    8.2 NVIDIA Pascal GPU

    8.3

    Denver 2 (Dual-Core) CPU cluster
    ARM Cortex-A57 QuadCore

    8.4 LPDDR4

    Low Power Double Data Rate 4

    8.4.1 介绍

    LPDDR可以说是全球范围内最广泛使用于移动设备的“工作记忆”内存。全新的20nm 8Gb LPDDR4内存,在性能和集成度上都比20纳米级4Gb LPDDR3内存提高一倍。 [1]
    LPDDR4可提供32Gbps的带宽,为DDR3 RAM的2倍。当前,Galaxy S5、Note 4和Nexus6均采用DDR3标准。更快速的RAM意味着应用的启动速度更快,这对于在执行多任务时启动重量级应用至关重要

    8.4.2 性能

    由于I/O接口数据传输速度最高可达3200Mbps,是通常使用的DDR3 DRAM的两倍,新推出的8Gb LPDDR4内存可以支持超高清影像的拍摄和播放,并能持续拍摄2000万像素的高清照片。
    与LPDDR3内存芯片相比,LPDDR4的运行电压降为1.1伏,堪称适用于大屏幕智能手机和平板电脑、高性能网络系统的最低功耗存储解决方案。以2GB内存封装为例,比起基于4Gb LPDDR3芯片的2GB内存封装,基于8Gb LPDDR4芯片的2GB内存封装因运行电压的降低和处理速度的提升,最大可节省40%的耗电量。同时,新产品的输入/输出信号传输采用三星独有的低电压摆幅终端逻辑(LVSTL, Low Voltage Swing Terminated Logic),不仅进一步降低了LPDDR4芯片的耗电量,并使芯片能在低电压下进行高频率运转,实现了电源使用效率的最优化。

    8.4.3 DDR3和DDR4的区别

    这两个玩穿了的内容,就不再阐述了,我找了一篇CSDN,在文末的推荐阅读3里面,不懂的建议看看,总体来说就是两者的频率和兼容主板都是不同的。所以总之,DDR4 和 DDR3 内存是无法通用的,两者不兼容。不过目前市场有 DDR4 和 DDR3 内存都支持的 100 系列主板,这种非主流主板需求量比较小,不太建议。既然配了100、200系列的主板,就直接使用DDR4就行了。

    8.5 eMMC & SSD

    • eMMC和SSD主要是满足不同需求而发展出来的NAND应用
    • eMMC
      平板和手机为了满足移动性的需求,所以需要做到轻,薄;尤其是功耗要很低,因此eMMC就诞生了;所以eMMC接口是用IO pin来定义的,这样接口简单,功耗低;另外eMMC对于苹果iPad、安卓平板电脑、手机的作用也是巨大的,平板和手机都比较小,所以eMMC是把控制器和NAND颗粒封装在一个package里面,这也造成eMMC不能放很多NAND颗粒,容量比较低。总结eMMC特点就是功耗低,容量小,随机读写性能差;
    • SSD
      SSD主要是为了满足大容量存储尤其是数据中心等应用场合,SSD成PC电脑的性能催化剂,读写性能尤其是随机读写性能快。为可达到这样的性能,SSD控制器都是使用高速总线,刚开始是SATA,现在PCIE也越来越多,以后可能会用光纤;NAND颗粒都有多个通道用于提升容量和读写性能。所以SSD功耗也很大。总结SSD特点就是功耗大, 容量大,读写快。
    • 推荐阅读4

    8.6 CAN & bus controller

    9 推荐阅读

    1. 在终端人工智能领域,NVIDIA的Jetson TX2为何如此受青睐?
      https://www.leiphone.com/news/201704/TEpCLJfL6ni05AJZ.html
    2. NVIDIA Jetson TX2 Development Kit
      https://www.jetsonhacks.com/2017/03/14/nvidia-jetson-tx2-development-kit/
    3. DDR扫盲——DDR与DDR2、DDR3的区别
      https://blog.csdn.net/chenzhen1080/article/details/82951214?utm_source=blogxgwz7
    4. emmc和ssd的区别
      https://blog.csdn.net/hawk_lexiang/article/details/78228789
    5. 软件堆栈和硬件堆栈概念分析
      http://www.elecfans.com/emb/587830.html
    6. NVIDIA Jetson TX2 Linux Benchmarks
      https://www.phoronix.com/scan.php?page=article&item=jetson-tegra-x2&num=1
    7. 官网embedded computing
      https://developer.nvidia.com/embedded-computing
    展开全文
  • jetson tx2驱动开发指南

    2019-02-18 14:16:36
    驱动开发的指南,对于学习上手jetson tx2的新手有很大的帮助
  • Jetson TX2 批量刷机

    2018-07-05 14:11:11
    最近工作需要,针对性的研究了一下Jetson TX2 批量刷机。文档是针对这几天的工作的总结,希望对大家有所帮助。
  • NVIDIA Jetson TX2使用

    千次阅读 2019-02-26 10:42:22
    NVIDIA Jetson TX2 刷机 Jetpack 3.2 教程 Jetson TX2入门之开箱刷机跑demo I run the demo successfully by using the command in this blog. NVIDIA TX2 安装ros Jetson TX2入门之ZED双目摄像头 TX2入门教程...
  • jetson tx2 pwm.docx

    2020-04-20 13:24:07
    里面有最权威的JETSON TX2 PWM介绍、配置和使用,资料是英文文档,从官方收集来的,介绍了设备树下如何对PWM进行配置
  • Jetson TX2 with DroneKit

    2020-12-04 17:14:31
    <p>I am trying to use nvidia jetson tx2 as companion computer on drone to control pixhawk cube and i am using dronekit python to control the drone via pixhawk cube. Could you please tell me how do i ...
  • <div><p>Hi I am trying to use the Jetson TX2 board to implement online SLAM. According to the docs, it says that that 16GB of RAM is the minimum requirement, however, the Jetson TX2 board only has 8GB...
  • Weird problem on Jetson TX2

    2020-12-02 00:23:21
    And I am deploying it on Jetson TX2. However, very weird things happen: the result on Jetson TX2 is totally different with my two servers(I trained a model on one server, and test it on another ...
  • NVIDIA Jetson TX2 SPI 程序

    2018-10-30 09:53:56
    NVIDIA JETSON TX2 spi接口调试过程,J21接口 spi 收发对接,收到数据 且对应代码内发送的数据,则接口调试成功
  • Crash jetson tx2

    2020-12-08 20:54:37
    I have an abnormal behavior, I launched the zed wrapper on the jetson tx2 and rviz on my laptop. At first it was all good, but lately the tx2 crashed (like a shutdown). So I tried to launch the zed ...
  • Support for Jetson TX2

    2020-12-25 19:25:44
    <div><p>how can i edit the these files to make support for Jetson TX2</p><p>该提问来源于开源项目:adafruit/Adafruit_Python_GPIO</p></div>
  • ROSPlan on nvidia jetson tx2

    2020-12-09 08:14:05
    <div><p>I bought a nvidia jetson tx2 and i'm trying to use ROSPlan on it. First of all there is an issue in the CmakeLists.txt file of rosplan_dependencies about the architecture of the jetson tx2...
  • Jetson TX2 之 JetPack 3.0 安装小记-附件资源
  • <div><p>I was wonder if I can run the adaptive learning framework on edge devices such as Jetson TX2 and Jetson Nano. Since the teacher model is a really large model and also the student model require...
  • 英伟达Jetson TX2 资源贴

    千次阅读 2019-01-23 19:16:19
    NVIDIA JETSON TX2 install packages 解决方案汇总 Jetson TX2刷机后USB无法使用 解决方案 Jetson TX2 开箱配置+刷机 侯同学在JesonTX2上配置pip NVIDIA开发者论坛 TX2上只能源码安装opencv,从Pycharm试过也不行,...
  • jetson tx2  [Jetson Download Center ][2]  [ how to install TensorFlow for Jetson TX2][3] DriveNet、SignNet、LaneNet、OpenRoadNet和WaitNet  [1]: ...

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