NVIDIA Jetson Linux Driver Package
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Nvidia Video Codec Sdk - Encoder
NVIDIA VIDEO CODEC SDK - ENCODER Application Note vNVENC_DA-6209-001_v14 | July 2021 Table of Contents Chapter 1. NVIDIA Hardware Video Encoder.......................................................................1 1.1. Introduction............................................................................................................................... 1 1.2. NVENC Capabilities...................................................................................................................1 1.3. NVENC Licensing Policy...........................................................................................................3 1.4. NVENC Performance................................................................................................................ 3 1.5. Programming NVENC...............................................................................................................5 1.6. FFmpeg Support....................................................................................................................... 5 NVIDIA VIDEO CODEC SDK - ENCODER vNVENC_DA-6209-001_v14 | ii Chapter 1. NVIDIA Hardware Video Encoder 1.1. Introduction NVIDIA GPUs - beginning with the Kepler generation - contain a hardware-based encoder (referred to as NVENC in this document) which provides fully accelerated hardware-based video encoding and is independent of graphics/CUDA cores. With end-to-end encoding offloaded to NVENC, the graphics/CUDA cores and the CPU cores are free for other operations. For example, in a game recording scenario, -
Performance Analysis and Optimization Opportunities for NVIDIA Automotive Gpus
Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs Hamid Tabani∗, Fabio Mazzocchetti∗y, Pedro Benedicte∗y, Jaume Abella∗ and Francisco J. Cazorla∗ ∗ Barcelona Supercomputing Center y Universitat Politecnica` de Catalunya Abstract—Advanced Driver Assistance Systems (ADAS) and products such as Renesas R-Car H3 [1], NVIDIA Jetson Autonomous Driving (AD) bring unprecedented performance TX2 [20] and NVIDIA Jetson Xavier [35], [27] have already requirements for automotive systems. Graphic Processing Unit reached the market building upon GPU technology inherited (GPU) based platforms have been deployed with the aim of meeting these requirements, being NVIDIA Jetson TX2 and from the high-performance domain. Automotive GPUs have its high-performance successor, NVIDIA AGX Xavier, relevant inherited designs devised for the high-performance domain representatives. However, to what extent high-performance GPU with the aim of reducing costs in the design, verification and configurations are appropriate for ADAS and AD workloads validation process for chip manufacturers. remains as an open question. Unfortunately, reusability of high-performance hardware This paper analyzes this concern and provides valuable does not consider GPUs efficiency in the automotive domain insights on this question by modeling two recent automotive NVIDIA GPU-based platforms, namely TX2 and AGX Xavier. In and, to the best of our knowledge, the design space for GPUs, particular, our work assesses their microarchitectural parameters where resources are sized with the aim of optimizing specific against relevant benchmarks, identifying GPU setups delivering goals such as performance, has not been yet thoroughly increased performance within a similar cost envelope, or decreas- performed for the automotive domain. ing hardware costs while preserving original performance levels. -
A Low-Cost Deep Neural Network-Based Autonomous Car
DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car Michael G. Bechtely, Elise McEllhineyy, Minje Kim?, Heechul Yuny y University of Kansas, USA. fmbechtel, elisemmc, [email protected] ? Indiana University, USA. [email protected] Abstract—We present DeepPicar, a low-cost deep neural net- task may be directly linked to the safety of the vehicle. This work based autonomous car platform. DeepPicar is a small scale requires a high computing capacity as well as the means to replication of a real self-driving car called DAVE-2 by NVIDIA. guaranteeing the timings. On the other hand, the computing DAVE-2 uses a deep convolutional neural network (CNN), which takes images from a front-facing camera as input and produces hardware platform must also satisfy cost, size, weight, and car steering angles as output. DeepPicar uses the same net- power constraints, which require a highly efficient computing work architecture—9 layers, 27 million connections and 250K platform. These two conflicting requirements complicate the parameters—and can drive itself in real-time using a web camera platform selection process as observed in [25]. and a Raspberry Pi 3 quad-core platform. Using DeepPicar, we To understand what kind of computing hardware is needed analyze the Pi 3’s computing capabilities to support end-to-end deep learning based real-time control of autonomous vehicles. for AI workloads, we need a testbed and realistic workloads. We also systematically compare other contemporary embedded While using a real car-based testbed would be most ideal, it computing platforms using the DeepPicar’s CNN-based real-time is not only highly expensive, but also poses serious safety control workload. -
Tegra Linux Driver Package
TEGRA LINUX DRIVER PACKAGE RN_05071-R32 | March 18, 2019 Subject to Change 32.1 Release Notes RN_05071-R32 Table of Contents 1.0 About this Release ................................................................................... 3 1.1 Login Credentials ............................................................................................... 4 2.0 Known Issues .......................................................................................... 5 2.1 General System Usability ...................................................................................... 5 2.2 Boot .............................................................................................................. 6 2.3 Camera ........................................................................................................... 6 2.4 CUDA Samples .................................................................................................. 7 2.5 Multimedia ....................................................................................................... 7 3.0 Top Fixed Issues ...................................................................................... 9 3.1 General System Usability ...................................................................................... 9 3.2 Camera ........................................................................................................... 9 4.0 Documentation Corrections ..................................................................... 10 4.1 Adaptation and Bring-Up Guide ............................................................................ -
Procurement of Electric-Electronic
REQUEST FOR QUOTATION (RFQ) Procurement of Electric-Electronic Laboratory Equipment and Supplies for Adana and Mersin Innovation Centers Adana ve Mersin Yenilik Merkezleri için Elektrik-Elektronik Laboratuvarları Ekipman ve Sarf Malzemeleri Alım İhalesi Please enter Name, Address & Contact Details of your Firm DATE: February 10, 2020 Lütfen Firma Adı, Adresi ve İrtibat Bilgilerini buraya giriniz TARİH: 10 Şubat 2020 REFERENCE: UNDP-TUR-RFQ(MC1)-2020/07 Dear Sir / Madam: Sayın İlgili: We kindly request you to submit your Teklife Davet Ek 1’deki Teknik Şartnamede ve Ek quotation for “Procurement of Electric-Electronic 2’de yer alan Malzeme Listesi’nde detayları verilmiş Laboratory Equipment and Supplies for Adana and olan “Adana ve Mersin Yenilik Merkezleri için Mersin Innovation Centers”, as detailed in Technical Elektrik-Elektronik Laboratuvarları Ekipman ve Sarf Specifications and Supplies List provided as Annex Malzemeleri Alım İhalesi” için teklifinizi sunmanızı 1 and Annex 2 (respectively) of this RFQ. When rica ederiz. preparing your quotation, please be guided by the forms attached hereto as ANNEX 1, ANNEX 2 and Teklifinizi lütfen EK 1, EK 2 ve EK 3’de sunulmuş ANNEX 3. formların rehberliğinde hazırlayınız. Quotations shall be submitted on or before 27 Teklifler aşağıda belirtilen adrese, 27 Şubat 2020, February 2020, 14:00 hrs. Turkey Local Time, via Türkiye saati ile 14:00’a kadar elden, e-posta veya hand delivery, e-mail or courier mail to the address kargo yolu ile teslim edilmelidir: below: United Nations Development Programme Birleşmiş Milletler Kalkınma Programı (UNDP) (UNDP) Yıldız Kule, Yukarı Dikmen Mahallesi, Turan Güneş Yıldız Kule, Yukarı Dikmen Mahallesi, Turan Bulvarı, No:106, 06550, Çankaya, Ankara/Türkiye Güneş Bulvarı, No:106, 06550, Çankaya, İlgili Kişi: Ümit ALSAÇ Ankara/Turkey Satın Alma Yetkilisi, UNDP Attn: Mr. -
Mechdyne-TGX-2.1-Installation-Guide
TGX Install Guide Version 2.1.3 Mechdyne Corporation March 2021 TGX INSTALL GUIDE VERSION 2.1.3 Copyright© 2021 Mechdyne Corporation All Rights Reserved. Purchasers of TGX licenses are given limited permission to reproduce this manual, provided the copies are for their use only and are not sold or distributed to third parties. All such copies must contain the title page and this notice page in their entirety. The TGX software program and accompanying documentation described herein are sold under license agreement. Their use, duplication, and disclosure are subject to the restrictions stated in the license agreement. Consistent with FAR 12.211 and 12.212, Commercial Computer Software, Computer Software Documentation, and Technical Data for Commercial Items are licensed to the U.S. Government under vendor's standard commercial license. This publication is provided “as is” without warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non- infringement. Any Mechdyne Corporation publication may include inaccuracies or typographical errors. Changes are periodically made to these publications, and changes may be incorporated in new editions. Mechdyne may improve or change its products described in any publication at any time without notice. Mechdyne assumes no responsibility for and disclaims all liability for any errors or omissions in this publication. Some jurisdictions do not allow the exclusion of implied warranties, so the above exclusion may not apply. TGX is a trademark of Mechdyne Corporation. Windows® is registered trademarks of Microsoft Corporation. Linux® is registered trademark of Linus Torvalds. -
NVIDIA Jetson TX2 Module
® ™ NVIDIA JETSON TX2 SUPERCOMPUTER ON A MODULE FOR AI AT THE EDGE TAKE REAL-TIME AI PERFORMANCE FARTHER WITH THE HIGH-PERFORMANCE, LOW-POWER JETSON TX2. The most innovative technology for AI computing and visual computing comes in a supercomputer the size of a credit card. Its small form factor and power envelope make the Jetson TX2 module ideal for intelligent edge devices like robots, drones, smart cameras, and portable medical devices. CONTENTS Jetson TX2 features a variety of standard hardware interfaces that make it easy to integrate it into a wide range of products and form factors. Plus, it comes with the complete Jetpack > NVIDIA Jetson TX2 > Attached Thermal Transfer Plate (TTP) SDK, which includes the BSP, libraries for deep learning, computer vision, GPU computing, multimedia processing, and much more to accelerate your software development. And it’s TECHNICAL SPECIFICATIONS supported by the Jetson developer site, which includes documentation, tutorials, and an FEATURES JETSON TX2 ecosystem of partners and developers. It’s never been easier to get started with AI. Graphics NVIDIA Pascal™, 256 NVIDIA For detailed specifications, design guides, Jetpack, and everything else you need to develop CUDA® cores with Jetson, go to developer.nvidia.com/embedded-computing. CPU HMP Dual Denver 2/2MB L2 + Quad ARM® A57/2MB L2 KEY FEATURES Video 4K x 2K 60 Hz Encode (HEVC) 4K x 2K 60 Hz Decode (12-bit Jetson TX2 Module I/O support) > NVIDIA Pascal™ architecture GPU > USB 3.0 Type A Memory 8 GB 128-bit LPDDR4 > Dual-core Denver 2 64-bit CPU -
Jetson TX2 Developer Kit User Guide
JETSON TX2 DEVELOPER KIT DA_09452-005 | December 17, 2019 ````` User Guide DOCUMENT CHANGE HISTORY DA_09452-005 Version Date Authors Description of Change Revision for Jetson Linux Driver 2.0 March 28, 2019 jsachs, plawrence Package r32.1. Revision for SDK Manager and Jetson 3.0 July 8, 2019 jsachs, plawrence Linux Driver Package r32.2. Revision for Jetson Linux Driver 4.0 December 17, 2019 ssheshadri Package r32.3.1. NOTE Welcome to the NVIDIA Jetson platform! There two key things you should do right away: 1. Sign up for the NVIDIA Developer Program – this enables you to ask questions and contribute on the NVIDIA Jetson Forums, gives access to all documentation and collateral on the Jetson Download Center, and more. 2. Read this User Guide! After that, check out these important links: • Jetson FAQ – Please read the FAQ. • Support Resources – This web page links to important resources, including the Jetson Forum and the Jetson Ecosystem page. • Jetson Linux Driver Package Release Notes – Jetson Linux Driver Package (L4T) is a key component of the Jetson platform, and provides the sample filesystem for your developer kit. Please read the latest release notes. • Thanks, The NVIDIA Jetson team Jetson TX2 Developer Kit DA_09452-005 | ii TABLE OF CONTENTS Note .........................................................................................ii Jetson TX2 Developer Kit ............................................................... 1 Included in the box ............................................................................. 1 Developer -
Debian GNU/Linux Installation Guide
Debian GNU/Linux Installation Guide July 31, 2021 Debian GNU/Linux Installation Guide Copyright © 2004 – 2021 the Debian Installer team This manual is free software; you may redistribute it and/or modify it under the terms of the GNU General Public License. Please refer to the license in Appendix F. Build version of this manual: 20210730. i Contents 1 Welcome to Debian 1 1.1 What is Debian? . 1 1.2 What is GNU/Linux? . 1 1.3 What is Debian GNU/Linux? . 2 1.4 What is the Debian Installer? . 3 1.5 Getting Debian . 3 1.6 Getting the Newest Version of This Document . 3 1.7 Organization of This Document . 3 1.8 About Copyrights and Software Licenses . 4 2 System Requirements 5 2.1 Supported Hardware . 5 2.1.1 Supported Architectures . 5 2.1.2 Three different ARM ports . 6 2.1.3 Variations in ARM CPU designs and support complexity . 6 2.1.4 Platforms supported by Debian/armhf . 6 2.1.5 Platforms no longer supported by Debian/armhf . 8 2.1.6 Multiple Processors . 8 2.1.7 Graphics Hardware Support . 8 2.1.8 Network Connectivity Hardware . 8 2.1.9 Peripherals and Other Hardware . 8 2.2 Devices Requiring Firmware . 8 2.3 Purchasing Hardware Specifically for GNU/Linux . 9 2.3.1 Avoid Proprietary or Closed Hardware . 9 2.4 Installation Media . 9 2.4.1 CD-ROM/DVD-ROM/BD-ROM . 9 2.4.2 Network . 10 2.4.3 Hard Disk . 10 2.4.4 Un*x or GNU system . -
NVIDIA RTX Virtual Workstation
NVIDIA RTX Virtual Workstation Sizing Guide NVIDIA RTX Virtual Workstation | 1 Executive Summary Document History nv-quadro-vgpu-deployment-guide-citrixonvmware-v2-082020 Version Date Authors Description of Change 01 Aug 17, 2020 AFS, JJC, EA Initial Release 02 Jan 08, 2021 CW Version 2 03 Jan 14, 2021 AFS Branding Update NVIDIA RTX Virtual Workstation | 2 Executive Summary Table of Contents Chapter 1. Executive Summary ......................................................................................................... 5 1.1 What is NVIDIA RTX vWS? ....................................................................................................... 5 1.2 Why NVIDIA vGPU? ................................................................................................................. 6 1.3 NVIDIA vGPU Architecture....................................................................................................... 6 1.4 Recommended NVIDIA GPU’s for NVIDIA RTX vWS ................................................................ 7 Chapter 2. Sizing Methodology......................................................................................................... 9 2.1 vGPU Profiles ........................................................................................................................... 9 2.2 vCPU Oversubscription .......................................................................................................... 10 Chapter 3. Tools ............................................................................................................................. -
Raspberry Pi Computer Vision Programming – Second Edition Programming – Second Edition
Raspberry Pi Computer Vision Pajankar Ashwin Edition Second – Programming Vision Computer Pi Raspberry Programming – Second Edition Raspberry Pi is one of the popular on Raspberry Pi, before covering major Raspberry Pi single-board computers of our generation. techniques and algorithms in image All the major image processing and computer processing, manipulation, and computer vision algorithms and operations can be vision. By sequentially working through the implemented easily with OpenCV on steps in each chapter, you'll understand Computer Vision Raspberry Pi. This updated second edition is essential OpenCV features. Later sections packed with cutting-edge examples and new will take you through creating graphical user topics, and covers the latest versions of key interface (GUI) apps with GPIO and OpenCV. technologies such as Python 3, Raspberry Pi, You'll also learn how to use the new computer Programming and OpenCV. This book will equip you with vision library, Mahotas, to perform various the skills required to successfully design and image processing operations. Finally, you'll implement your own OpenCV, Raspberry Pi, explore the Jupyter notebook and how to and Python-based computer vision projects. set up a Windows computer and Ubuntu for computer vision. Second Edition At the start, you'll learn the basics of Python 3, and the fundamentals of single-board By the end of this book, you'll be able to computers and NumPy. Next, you'll discover confi dently build and deploy computer how to install OpenCV 4 for Python 3 vision apps. Design -
NVIDIA A100 Tensor Core GPU Architecture UNPRECEDENTED ACCELERATION at EVERY SCALE
NVIDIA A100 Tensor Core GPU Architecture UNPRECEDENTED ACCELERATION AT EVERY SCALE V1.0 Table of Contents Introduction 7 Introducing NVIDIA A100 Tensor Core GPU - our 8th Generation Data Center GPU for the Age of Elastic Computing 9 NVIDIA A100 Tensor Core GPU Overview 11 Next-generation Data Center and Cloud GPU 11 Industry-leading Performance for AI, HPC, and Data Analytics 12 A100 GPU Key Features Summary 14 A100 GPU Streaming Multiprocessor (SM) 15 40 GB HBM2 and 40 MB L2 Cache 16 Multi-Instance GPU (MIG) 16 Third-Generation NVLink 16 Support for NVIDIA Magnum IO™ and Mellanox Interconnect Solutions 17 PCIe Gen 4 with SR-IOV 17 Improved Error and Fault Detection, Isolation, and Containment 17 Asynchronous Copy 17 Asynchronous Barrier 17 Task Graph Acceleration 18 NVIDIA A100 Tensor Core GPU Architecture In-Depth 19 A100 SM Architecture 20 Third-Generation NVIDIA Tensor Core 23 A100 Tensor Cores Boost Throughput 24 A100 Tensor Cores Support All DL Data Types 26 A100 Tensor Cores Accelerate HPC 28 Mixed Precision Tensor Cores for HPC 28 A100 Introduces Fine-Grained Structured Sparsity 31 Sparse Matrix Definition 31 Sparse Matrix Multiply-Accumulate (MMA) Operations 32 Combined L1 Data Cache and Shared Memory 33 Simultaneous Execution of FP32 and INT32 Operations 34 A100 HBM2 and L2 Cache Memory Architectures 34 ii NVIDIA A100 Tensor Core GPU Architecture A100 HBM2 DRAM Subsystem 34 ECC Memory Resiliency 35 A100 L2 Cache 35 Maximizing Tensor Core Performance and Efficiency for Deep Learning Applications 37 Strong Scaling Deep Learning