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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. -
NVIDIA Launches Tegra X1 Mobile Super Chip
NVIDIA Launches Tegra X1 Mobile Super Chip Maxwell GPU Architecture Delivers First Teraflops Mobile Processor, Powering Deep Learning and Computer Vision Applications NVIDIA today unveiled Tegra® X1, its next-generation mobile super chip with over one teraflops of processing power – delivering capabilities that open the door to unprecedented graphics and sophisticated deep learning and computer vision applications. Tegra X1 is built on the same NVIDIA Maxwell™ GPU architecture rolled out only months ago for the world's top-performing gaming graphics card, the GeForce® GTX 980. The 256-core Tegra X1 provides twice the performance of its predecessor, the Tegra K1, which is based on the previous-generation Kepler™ architecture and debuted at last year's Consumer Electronics Show. Tegra processors are built for embedded products, mobile devices, autonomous machines and automotive applications. Tegra X1 will begin appearing in the first half of the year. It will be featured in the newly announced NVIDIA DRIVE™ car computers. DRIVE PX is an auto-pilot computing platform that can process video from up to 12 onboard cameras to run capabilities providing Surround-Vision, for a seamless 360-degree view around the car, and Auto-Valet, for true self-parking. DRIVE CX is a complete cockpit platform designed to power the advanced graphics required across the increasing number of screens used for digital clusters, infotainment, head-up displays, virtual mirrors and rear-seat entertainment. "We see a future of autonomous cars, robots and drones that see and learn, with seeming intelligence that is hard to imagine," said Jen-Hsun Huang, CEO and co-founder, NVIDIA. -
Co-Optimizing Performance and Memory Footprint Via Integrated CPU/GPU Memory Management, an Implementation on Autonomous Driving Platform
Co-Optimizing Performance and Memory Footprint Via Integrated CPU/GPU Memory Management, an Implementation on Autonomous Driving Platform Soroush Bateni*, Zhendong Wang*, Yuankun Zhu, Yang Hu, and Cong Liu The University of Texas at Dallas Abstract—Cutting-edge embedded system applications, such as launches the OpenVINO toolkit for the edge-based deep self-driving cars and unmanned drone software, are reliant on learning inference on its integrated HD GPUs [4]. integrated CPU/GPU platforms for their DNNs-driven workload, Despite the advantages in SWaP features presented by the such as perception and other highly parallel components. In this work, we set out to explore the hidden performance im- integrated CPU/GPU architecture, our community still lacks plication of GPU memory management methods of integrated an in-depth understanding of the architectural and system CPU/GPU architecture. Through a series of experiments on behaviors of integrated GPU when emerging autonomous and micro-benchmarks and real-world workloads, we find that the edge intelligence workloads are executed, particularly in multi- performance under different memory management methods may tasking fashion. Specifically, in this paper we set out to explore vary according to application characteristics. Based on this observation, we develop a performance model that can predict the performance implications exposed by various GPU memory system overhead for each memory management method based management (MM) methods of the integrated CPU/GPU on application characteristics. Guided by the performance model, architecture. The reason we focus on the performance impacts we further propose a runtime scheduler. By conducting per-task of GPU MM methods are two-fold. -
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. -
Developing & Deploying Autonomous Driving
DEVELOPING & DEPLOYING AUTONOMOUS DRIVING APPLICATIONS WITH THE NVIDIA DRIVE PX PLATFORM Shri Sundaram, Product Manager, DRIVE PX Platform DRIVE PX: AV Development Platform AV Developers: DRIVE PX as your tool AV HW/SW Ecosystem: DRIVE PX as your platform to reach developers 2 NVIDIA DRIVE PX Open AV Computing Platform for the Transportation Industry Powerful and scalable AV computer Deep Neural Network, Sensor Fusion and Computing Extensive I/O to interface with wide range of sensors and vehicle networks An open SW stack Level 3 to Level 5; ASIL-D functional safety 3 DRIVE PX DRIVING AV AI DRIVE Platform Engagements 400 350 300 250 Launched CES 2015 200 150 Spike in AV AI engagements after 100 50 we powered on discrete GPU 0 FY18 Q1 FY18 Q3 More than doubled in last 6 months Plus >145 AV Startups on NVIDIA DRIVE Source: NVIDIA statistics 4 AV DEVELOPMENT Path from Idea to Production IDEA DEVELOPMENT PROTOTYPE PRODUCTION Develop Test Deploy Perception Feature development Safety hardening OBJECTIVE Mapping/Localization Validation Performance tuning Path Planning SW upgrades Combination/More… PC PC DRIVE Platform TOOL Automotive Sensors Production SW OTA framework Scalable compute with discrete GPUs Ecosystem of sensors + other HW/SW peripherals TensorRT, CUDA, Open Source Frameworks 5 Autonomous Vehicle DEVELOPMENT FLOW Application Development 4 Using DRIVE PX Platform Test/Drive 3 1 Data Acquired Curated/Annotated Neural Network Deep Neural From Sensors Training Data Training Network Autonomous Vehicle Applications 2 1 Data Acquisition -
Diseño De Un Prototipo Electrónico Para El Control Automático De La Luz Alta De Un Vehículo Mediante Detección Inteligente De Otros Automóviles.”
Tecnológico de Costa Rica Escuela Ingeniería Electrónica Proyecto de Graduación “Diseño de un prototipo electrónico para el control automático de la luz alta de un vehículo mediante detección inteligente de otros automóviles.” Informe de Proyecto de Graduación para optar por el título de Ingeniero en Electrónica con el grado académico de Licenciatura Ronald Miranda Arce San Carlos, 2018 2 Declaración de autenticidad Yo, Ronald Miranda Arce, en calidad de estudiante de la carrera de ingeniería Electrónica, declaro que los contenidos de este informe de proyecto de graduación son absolutamente originales, auténticos y de exclusiva responsabilidad legal y académica del autor. Ronald Fernando Miranda Arce Santa Clara, San Carlos, Costa Rica Cédula: 207320032 3 Resumen Se presenta el diseño y prueba de un prototipo electrónico para el control automático de la luz alta de un vehículo mediante la detección inteligente de otros automóviles, de esta forma mejorar la experiencia de manejo y seguridad al conducir en la noche, disminuyendo el riesgo de accidentes por deslumbramiento en las carreteras. Se expone el uso de machine learning (máquina de aprendizaje automático) para el entrenamiento de una red neuronal, que permita el reconocimiento en profundidad de patrones, en este caso identificar el patrón que describe un vehículo cuando se acerca durante la noche. Se entrenó exitosamente una red neuronal capaz de identificar correctamente hasta un 89% de los casos ocurridos en las pruebas realizadas, generando las señales para controlar de forma automática los cambios de luz. 4 Summary The design and testing of an electronic prototype for the automatic control of the vehicle's high light through the intelligent detection of other automobiles is presented. -
Introduction 3 Acknowledgement 3 Problem and Project Statement 3
Introduction 3 Acknowledgement 3 Problem and Project Statement 3 Operational environment 5 Intended uses and users 5 Assumptions and limitations 6 End Product 7 Specifications and analysis 9 Proposed Design 9 Design Analysis 10 Implementation details 10 Testing process and testing results 11 Placing your work in the context of: 21 • Related products 21 • Related literature If/When needed, you should resort to Appendices. 22 Appendix I – Operation Manual 23 Raspberry PI 3 Model B 23 Nvidia Drive PX2 29 Software Licensing 31 Appendix II: Alternative/ other initial versions of the design 32 1) The DSRC 32 2) The Mobile Wireless Data 32 3) The Arduino & XBEE 32 Appendix III: Other Considerations 34 Appendix IV: Code 35 Introduction 35 External Libraries 35 Native Libraries 35 Gather Component Information 35 Finding and Initializing Devices 36 Initialize The GPS 37 Parsing and Transmission 37 Introduction As part of our senior design we had to create the means for the communication between two vehicles. The information that had to be communicated was the latitude, longitude coordinates, and the time. For this we have come up with several solutions throughout our design and testing process. We looked into DSRC, Cellular Communication, Raspberry Pi’s over a LAN network, and Xbee transceivers. All these are viable options and several can complement the others pretty well. We will have more details in following sections. Acknowledgement First off, we would like to formally thank Dr. Hegde for guidance on our project and the insightful advice on how to approach and overcome different obstacles of our progress. Also we would like to thank Vishal as our project manager who made sure we stuck to our milestones and meeting with us every week to make sure we were working towards our goals. -
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. -
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 . -
Deeprcar: an Autonomous Car Model Student: Bc
ASSIGNMENT OF MASTER’S THESIS Title: DeepRCar: An Autonomous Car Model Student: Bc. David Ungurean Supervisor: Ing. Zdeněk Buk, Ph.D. Study Programme: Informatics Study Branch: Knowledge Engineering Department: Department of Applied Mathematics Validity: Until the end of winter semester 2019/20 Instructions Search for RC car models suitable for autonomous driving implementation. Focus on models which allow the control software to be run onboard. The autonomous control will be based primarily on visual inputs from an onboard camera. Other sensors such as ultrasonic distance sensors can be also used. Implement the control algorithms using deep neural networks which use the visual information as input and produce control signals for steering and speed control. Design and implement several experiments to test various neural network architectures and image preprocessing methods. References - J. Koutnik, J. Schmidhuber, F. Gomez, Online evolution of deep convolutional network for vision-based RL, SAB 2014: http://people.idsia.ch/ koutnik/papers/koutnik2014sab.pdf - Zheng Wang, Self Driving RC Car: https://zhengludwig.wordpress.com/projects/self-driving-rc-car/ Ing. Karel Klouda, Ph.D. doc. RNDr. Ing. Marcel Jiřina, Ph.D. Head of Department Dean Prague February 16, 2018 Master’s thesis DeepRCar: An Autonomous Car Model Bc. David Ungurean Department of Applied Mathematics Supervisor: Ing. Zdenˇek Buk, Ph.d. May 9, 2018 Acknowledgements First of all, I would like to thank my supervisor Dr. Buk for giving me the chance to explore such an interesting field of machine learning and for always pointing me in the right direction, whenever I was starting to get lost. I am also very grateful to Dr.