Aerocore 2 for NVIDIA® Jetson™

Total Page：16

File Type：pdf, Size：1020Kb

Aerocore 2 for NVIDIA® Jetson™ Parallel Processing Power for Robotic and Edge Computing Devices ® REDWOOD CITY, CA. May 18, 2018— Gumstix , Inc., the leader in design-to-order embedded systems, announces the coming release of the Aerocore 2 for NVIDIA® Jetson™. Designed for supercomputing, this new AeroCore 2 puts the parallel processing power of the NVIDIA Jetson TX1 and TX2 on a robotics-inspired development board. The latest addition to the Aerocore 2 family will bring the NVIDIA Tegra’s 256 CUDA cores to the skies, along with up to 4 HD camera modules, USB 3.0, LTE connectivity, and micro-HDMI. Perform on-board image recognition and deep learning in your next robotics or aerial design with the Aerocore 2 for NVIDIA® Jetson™. Available this June 2018 online at gumstix.com for $ 265.00. “Gumstix has spent 15 years supporting the fastest and most complex embedded electronics. Our Aerocore 2 for NVIDIA® Jetson™ will support advanced visual processing,” said Gordon Kruberg, Gumstix CEO, “ And with Geppetto, custom robotic devices can be designed and shipped in 15 business days.” ® The Gumstix Aerocore 2 product line, developed in Geppetto , is a proven robotics design combining real-time autopilot firmware and high-level processing capabilities, employing an ARM Cortex M4 microcontroller with PX4 firmware, PWMs, serial buses, and GPS along with powerful Linux devices. As with all AeroCore boards, the Aerocore 2 for NVIDIA® Jetson™can be customized in minutes online with Geppetto® ### About Gumstix, Inc. As a global leader in design-to-order hardware and manufacturing solutions. Gumstix® gives its customers the power to solve their electronic design challenges with Geppetto® D2O -- the online design-to-order system-- and a broad portfolio of small computers and ® embedded boards. In addition to engineers and industrial designers, Gumstix helps students, educators, and makers unlock their creative ideas to bring them to market.. Gumstix systems have launched some of the world’s coolest products - from phones to drones - on commercial, university, and hobbyist workbenches in over 45 countries. For more information, visit www.gumstix.com .

Copy Link

Recommended publications

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.
[Show full text]
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.
[Show full text]
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.
[Show full text]
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
[Show full text]
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
[Show full text]
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 .
[Show full text]
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
[Show full text]
Adaptive Body Area Sensor Net- Work for Iot Based Remote Healthcare
Humayun Rashid ADAPTIVE BODY AREA SENSOR NET- WORK FOR IOT BASED REMOTE HEALTHCARE Faculty of Information Technology and Communication Sciences Master of Science Thesis June 2019 ii ABSTRACT Humayun Rashid: Adaptive Body Area Sensor Network for IoT Based Remote Healthcare Master of Science Thesis Tampere University Master's Programme in Information Technology, Communication Systems and Networks June 2019 Chronic health condition is considered as one of the key significant reason of death in the modern world. Chronic conditions are long lasting disorders of human beings that commonly can be controlled but a complete recovery is not possible for most of the cases. Persons with chronic health issues face various signs or symptoms of illness every day. It has an impact on their quality of life that can reduce life expectancy. data collection in medicine and healthcare performs a very important role as researchers utilize chronic disorder data to find out the proper method to reduce the impact of chronic disorders. The traditional method of data collection is complicated and does not provide enough support for remote patient monitoring. An Internet of things (IoT) based archi- tectures based on Wireless body area Network (WBASN) can be employed to solve this problem. A key limitation of an IoT based remote patient monitoring system is that the patient monitoring period is greatly influenced by the wearable sensor node’s power consumption. In the first phase of our work, a multi-goal multi-policy IoT-based patient monitoring system is presented that has utilized a self-aware power manage. It is tested and compared to a baseline system to study the higher power efficiency of the developed sensor node.
[Show full text]
Tegra Linux Driver Package
TEGRA LINUX DRIVER PACKAGE RN_05071-R32 | July 17, 2019 Subject to Change 32.2 Release Notes RN_05071-R32 Table of Contents 1.0 About this Release ................................................................................... 4 1.1 Login Credentials ............................................................................................... 5 2.0 Known Issues .......................................................................................... 6 2.1 General System Usability ...................................................................................... 6 2.2 Boot .............................................................................................................. 7 2.3 Camera ........................................................................................................... 8 2.4 Connectivity ..................................................................................................... 9 2.5 CUDA ........................................................................................................... 10 2.6 Jetson Developer Kit ......................................................................................... 10 2.7 Multimedia ..................................................................................................... 10 2.8 SDK Manager .................................................................................................. 11 3.0 Top Fixed Issues .................................................................................... 12 3.1 General System Usability ...................................................................................
[Show full text]
A Leader in AI Computing～
Special Feature 1: NVIDIA Special Feature NVIDIA ～A Leader in AI Computing～ Born in Silicon Valley in 1993, NVIDIA is the inventor of the GPU and a world leader in visual computing. Along with the NVIDIA solutions that continue to elevate its reputation as an 'AI computing company', we also offer a wide variety of solutions including autonomous driving, the latest vehicle technology from NVIDIA and NEXTY Electronics. Who is NVIDIA? Since NVIDIA's establishment in 1993, the GPUs conceived by the company have driven growth in the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. In more recent years, GPU-based deep learning has become a catalyst Established: 1993 for modern AI and is paving the way to a new age of computing. Founder: Jensen Huang These GPUs are being used as the backbone of a diverse array of Address: Santa Clara, California, U.S. computers, robotics, and autonomous vehicles that are aware of― Number of employees: Approximately 12,000 and understand―the world around them. NVIDIA is no longer just a semiconductor supplier. Today, NVIDIA's reputation as an 'AI Sales volume: FY 2018, USD 9.71 billion (over 1 trillion yen) computing company' is making another leap forward. "Endeavor" is Nvidia's new ofﬁ ce building located Jensen Huang, founder and CEO, is NVIDIA's Released in August 2018, the GeForce RTX in Santa Clara, the heart of Silicon Valley. Please iconic leader. He was selected as 'Best- realized real-time ray tracing with the newest stop by if you are ever in Silicon Valley.
[Show full text]
NVIDIA Jetson TX1 System-On-Module
NVIDIA® JETSON™ TX1 SYSTEM-ON-MODULE FULL-FEATURED MODULE FOR INTELLIGENT DEVICES JETSON TX1 IS A FULL-FEATURED SUPERCOMPUTER ON A MODULE THE SIZE OF A CREDIT CARD. This technology breakthrough gives you the performance and power efficiency to create next-generation products targeting GPU computing, computer graphics, and artificial intelligence (AI). Jetson TX1 provides a variety of standard hardware interfaces that can be exposed using a CONTENTS carrier board, enabling a highly flexible and extensible platform. It’s the ideal solution for > NVIDIA Jetson TX1 all your use cases that demand high computational performance in a low-power envelope. > Attached Thermal Transfer Plate (TTP) The module is supported by NVIDIA’s complete development tool chain, including a variety of common APIs. The Jetson SDK, JetPack, includes a Linux BSP and sample filesystem TECHNICAL SPECIFICATIONS for your device, developer tools, libraries for AI, Compute, Multimedia, and Graphics, Jetson TX1 System-on-Module supporting documentation, and code samples to help you get started. GPU 1 TFLOP/s 256-core Maxwell For detailed specifications, design guides, and JetPack, go to CPU 64-bit quad-core ARM http://developer.nvidia.com/embedded-computing. A57 CPUs Memory 4 GB LPDDR4 | 25.6 GB/s KEY FEATURES Storage 16 GB eMMC Jetson TX1 System-on-Module Software I/O Networking 10/100/1000Mbit ™ > NVIDIA Maxwell GPU with 256 NVIDIA > Linux for Tegra driver > USB 3.0 Ethernet ® CUDA cores package, including > USB 2.0 Camera 12 CSI DPHY 1.1 lanes ® ® Ubuntu-based sample
[Show full text]
A Portable Fuzzy Driver Drowsiness Estimation System
sensors Article A Portable Fuzzy Driver Drowsiness Estimation System Alimed Celecia 1, Karla Figueiredo 2,*, Marley Vellasco 1,* and René González 3 1 Electrical Engineering Department, PUC-Rio, Rio de Janeiro 22451900, Brazil; [email protected] 2 Department of Informatics and Computer Science, Institute of Mathematics and Statistics, State University of Rio de Janeiro (UERJ), Rio de Janeiro 20550-900, Brazil 3 Research & Development Department, Solinftec, Araçatuba 16013337, Brazil; [email protected] * Correspondence: [email protected] (K.F.); [email protected] (M.V.) Received: 31 May 2020; Accepted: 6 July 2020; Published: 23 July 2020 Abstract: The adequate automatic detection of driver fatigue is a very valuable approach for the prevention of traffic accidents. Devices that can determine drowsiness conditions accurately must inherently be portable, adaptable to different vehicles and drivers, and robust to conditions such as illumination changes or visual occlusion. With the advent of a new generation of computationally powerful embedded systems such as the Raspberry Pi, a new category of real-time and low-cost portable drowsiness detection systems could become standard tools. Usually, the proposed solutions using this platform are limited to the definition of thresholds for some defined drowsiness indicator or the application of computationally expensive classification models that limits their use in real-time. In this research, we propose the development of a new portable, low-cost, accurate, and robust drowsiness recognition device. The proposed device combines complementary drowsiness measures derived from a temporal window of eyes (PERCLOS, ECD) and mouth (AOT) states through a fuzzy inference system deployed in a Raspberry Pi with the capability of real-time response.
[Show full text]