NVIDIA Tegra K1 a New Era in Mobile Computing
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Bootstomp: on the Security of Bootloaders in Mobile Devices
BootStomp: On the Security of Bootloaders in Mobile Devices Nilo Redini, Aravind Machiry, Dipanjan Das, Yanick Fratantonio, Antonio Bianchi, Eric Gustafson, Yan Shoshitaishvili, Christopher Kruegel, and Giovanni Vigna, UC Santa Barbara https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/redini This paper is included in the Proceedings of the 26th USENIX Security Symposium August 16–18, 2017 • Vancouver, BC, Canada ISBN 978-1-931971-40-9 Open access to the Proceedings of the 26th USENIX Security Symposium is sponsored by USENIX BootStomp: On the Security of Bootloaders in Mobile Devices Nilo Redini, Aravind Machiry, Dipanjan Das, Yanick Fratantonio, Antonio Bianchi, Eric Gustafson, Yan Shoshitaishvili, Christopher Kruegel, and Giovanni Vigna fnredini, machiry, dipanjan, yanick, antoniob, edg, yans, chris, [email protected] University of California, Santa Barbara Abstract by proposing simple mitigation steps that can be im- plemented by manufacturers to safeguard the bootloader Modern mobile bootloaders play an important role in and OS from all of the discovered attacks, using already- both the function and the security of the device. They deployed hardware features. help ensure the Chain of Trust (CoT), where each stage of the boot process verifies the integrity and origin of 1 Introduction the following stage before executing it. This process, in theory, should be immune even to attackers gaining With the critical importance of the integrity of today’s full control over the operating system, and should pre- mobile and embedded devices, vendors have imple- vent persistent compromise of a device’s CoT. However, mented a string of inter-dependent mechanisms aimed at not only do these bootloaders necessarily need to take removing the possibility of persistent compromise from untrusted input from an attacker in control of the OS in the device. -
Gs-35F-4677G
March 2013 NCS Technologies, Inc. Information Technology (IT) Schedule Contract Number: GS-35F-4677G FEDERAL ACQUISTIION SERVICE INFORMATION TECHNOLOGY SCHEDULE PRICELIST GENERAL PURPOSE COMMERCIAL INFORMATION TECHNOLOGY EQUIPMENT Special Item No. 132-8 Purchase of Hardware 132-8 PURCHASE OF EQUIPMENT FSC CLASS 7010 – SYSTEM CONFIGURATION 1. End User Computer / Desktop 2. Professional Workstation 3. Server 4. Laptop / Portable / Notebook FSC CLASS 7-25 – INPUT/OUTPUT AND STORAGE DEVICES 1. Display 2. Network Equipment 3. Storage Devices including Magnetic Storage, Magnetic Tape and Optical Disk NCS TECHNOLOGIES, INC. 7669 Limestone Drive Gainesville, VA 20155-4038 Tel: (703) 621-1700 Fax: (703) 621-1701 Website: www.ncst.com Contract Number: GS-35F-4677G – Option Year 3 Period Covered by Contract: May 15, 1997 through May 14, 2017 GENERAL SERVICE ADMINISTRATION FEDERAL ACQUISTIION SERVICE Products and ordering information in this Authorized FAS IT Schedule Price List is also available on the GSA Advantage! System. Agencies can browse GSA Advantage! By accessing GSA’s Home Page via Internet at www.gsa.gov. TABLE OF CONTENTS INFORMATION FOR ORDERING OFFICES ............................................................................................................................................................................................................................... TC-1 SPECIAL NOTICE TO AGENCIES – SMALL BUSINESS PARTICIPATION 1. Geographical Scope of Contract ............................................................................................................................................................................................................................. -
FAN53525 3.0A, 2.4Mhz, Digitally Programmable Tinybuck® Regulator
FAN53525 — 3.0 A, 2.4 MHz, June 2014 FAN53525 3.0A, 2.4MHz, Digitally Programmable TinyBuck® Regulator Digitally Programmable TinyBuck Digitally Features Description . Fixed-Frequency Operation: 2.4 MHz The FAN53525 is a step-down switching voltage regulator that delivers a digitally programmable output from an input . Best-in-Class Load Transient voltage supply of 2.5 V to 5.5 V. The output voltage is 2 . Continuous Output Current Capability: 3.0 A programmed through an I C interface capable of operating up to 3.4 MHz. 2.5 V to 5.5 V Input Voltage Range Using a proprietary architecture with synchronous . Digitally Programmable Output Voltage: rectification, the FAN53525 is capable of delivering 3.0 A - 0.600 V to 1.39375 V in 6.25 mV Steps continuous at over 80% efficiency, maintaining that efficiency at load currents as low as 10 mA. The regulator operates at Programmable Slew Rate for Voltage Transitions . a nominal fixed frequency of 2.4 MHz, which reduces the . I2C-Compatible Interface Up to 3.4 Mbps value of the external components to 330 nH for the output inductor and as low as 20 µF for the output capacitor. PFM Mode for High Efficiency in Light Load . Additional output capacitance can be added to improve . Quiescent Current in PFM Mode: 50 µA (Typical) regulation during load transients without affecting stability, allowing inductance up to 1.2 µH to be used. Input Under-Voltage Lockout (UVLO) ® At moderate and light loads, Pulse Frequency Modulation Regulator Thermal Shutdown and Overload Protection . (PFM) is used to operate in Power-Save Mode with a typical . -
Embedded Computer Solutions for Advanced Automation Control «
» Embedded Computer Solutions for Advanced Automation Control « » Innovative Scalable Hardware » Qualifi ed for Industrial Software » Open Industrial Communication The pulse of innovation » We enable Automation! « Open Industrial Automation Platforms Kontron, one of the leaders of embedded computing technol- ogy has established dedicated global business units to provide application-ready OEM platforms for specifi c markets, includ- ing Industrial Automation. With our global corporate headquarters located in Germany, Visualization & Control Data Storage Internet-of-Things and regional headquarters in the United States and Asia-Pa- PanelPC Industrial Server cifi c, Kontron has established a strong presence worldwide. More than 1000 highly qualifi ed engineers in R&D, technical Industrie 4.0 support, and project management work with our experienced sales teams and sales partners to devise a solution that meets M2M SYMKLOUD your individual application’s demands. When it comes to embedded computing, you can focus on your core capabilities and rely on Kontron as your global OEM part- ner for a successful long-term business relationship. In addition to COTS standards based products, Kontron also of- fers semi- and full-custom ODM services for a full product port- folio that ranges from Computer-on-Modules and SBCs, up to embedded integrated systems and application ready platforms. Open for new technologies Kontron provides an exceptional range of hardware for any kind of control solution. Open for individual application Kontron systems are available either as readily integrated control solutions, or as open platforms for customers who build their own control applications with their own look and feel. Open for real-time Kontron’s Industrial Automation platforms are open for Real- Industrial Ethernet Time operating systems like VxWorks and Linux with real time extension. -
Data Sheet: Quadro GV100
REINVENTING THE WORKSTATION WITH REAL-TIME RAY TRACING AND AI NVIDIA QUADRO GV100 The Power To Accelerate AI- FEATURES > Four DisplayPort 1.4 Enhanced Workflows Connectors3 The NVIDIA® Quadro® GV100 reinvents the workstation > DisplayPort with Audio to meet the demands of AI-enhanced design and > 3D Stereo Support with Stereo Connector3 visualization workflows. It’s powered by NVIDIA Volta, > NVIDIA GPUDirect™ Support delivering extreme memory capacity, scalability, and > NVIDIA NVLink Support1 performance that designers, architects, and scientists > Quadro Sync II4 Compatibility need to create, build, and solve the impossible. > NVIDIA nView® Desktop SPECIFICATIONS Management Software GPU Memory 32 GB HBM2 Supercharge Rendering with AI > HDCP 2.2 Support Memory Interface 4096-bit > Work with full fidelity, massive datasets 5 > NVIDIA Mosaic Memory Bandwidth Up to 870 GB/s > Enjoy fluid visual interactivity with AI-accelerated > Dedicated hardware video denoising encode and decode engines6 ECC Yes NVIDIA CUDA Cores 5,120 Bring Optimal Designs to Market Faster > Work with higher fidelity CAE simulation models NVIDIA Tensor Cores 640 > Explore more design options with faster solver Double-Precision Performance 7.4 TFLOPS performance Single-Precision Performance 14.8 TFLOPS Enjoy Ultimate Immersive Experiences Tensor Performance 118.5 TFLOPS > Work with complex, photoreal datasets in VR NVIDIA NVLink Connects 2 Quadro GV100 GPUs2 > Enjoy optimal NVIDIA Holodeck experience NVIDIA NVLink bandwidth 200 GB/s Realize New Opportunities with AI -
NVIDIA Quadro P4000
NVIDIA Quadro P4000 GP104 1792 112 64 8192 MB GDDR5 256 bit GRAPHICS PROCESSOR CORES TMUS ROPS MEMORY SIZE MEMORY TYPE BUS WIDTH The Quadro P4000 is a professional graphics card by NVIDIA, launched in February 2017. Built on the 16 nm process, and based on the GP104 graphics processor, the card supports DirectX 12.0. The GP104 graphics processor is a large chip with a die area of 314 mm² and 7,200 million transistors. Unlike the fully unlocked GeForce GTX 1080, which uses the same GPU but has all 2560 shaders enabled, NVIDIA has disabled some shading units on the Quadro P4000 to reach the product's target shader count. It features 1792 shading units, 112 texture mapping units and 64 ROPs. NVIDIA has placed 8,192 MB GDDR5 memory on the card, which are connected using a 256‐bit memory interface. The GPU is operating at a frequency of 1227 MHz, which can be boosted up to 1480 MHz, memory is running at 1502 MHz. We recommend the NVIDIA Quadro P4000 for gaming with highest details at resolutions up to, and including, 5760x1080. Being a single‐slot card, the NVIDIA Quadro P4000 draws power from 1x 6‐pin power connectors, with power draw rated at 105 W maximum. Display outputs include: 4x DisplayPort. Quadro P4000 is connected to the rest of the system using a PCIe 3.0 x16 interface. The card measures 241 mm in length, and features a single‐slot cooling solution. Graphics Processor Graphics Card GPU Name: GP104 Released: Feb 6th, 2017 Architecture: Pascal Production Active Status: Process Size: 16 nm Bus Interface: PCIe 3.0 x16 Transistors: 7,200 -
Graphics Card Support List
Graphics card support list Device Name Chipset ASUS GTXTITAN-6GD5 NVIDIA GeForce GTX TITAN ZOTAC GTX980 NVIDIA GeForce GTX980 ASUS GTX980-4GD5 NVIDIA GeForce GTX980 MSI GTX980-4GD5 NVIDIA GeForce GTX980 Gigabyte GV-N980D5-4GD-B NVIDIA GeForce GTX980 MSI GTX970 GAMING 4G GOLDEN EDITION NVIDIA GeForce GTX970 Gigabyte GV-N970IXOC-4GD NVIDIA GeForce GTX970 ASUS GTX780TI-3GD5 NVIDIA GeForce GTX780Ti ASUS GTX770-DC2OC-2GD5 NVIDIA GeForce GTX770 ASUS GTX760-DC2OC-2GD5 NVIDIA GeForce GTX760 ASUS GTX750TI-OC-2GD5 NVIDIA GeForce GTX750Ti ASUS ENGTX560-Ti-DCII/2D1-1GD5/1G NVIDIA GeForce GTX560Ti Gigabyte GV-NTITAN-6GD-B NVIDIA GeForce GTX TITAN Gigabyte GV-N78TWF3-3GD NVIDIA GeForce GTX780Ti Gigabyte GV-N780WF3-3GD NVIDIA GeForce GTX780 Gigabyte GV-N760OC-4GD NVIDIA GeForce GTX760 Gigabyte GV-N75TOC-2GI NVIDIA GeForce GTX750Ti MSI NTITAN-6GD5 NVIDIA GeForce GTX TITAN MSI GTX 780Ti 3GD5 NVIDIA GeForce GTX780Ti MSI N780-3GD5 NVIDIA GeForce GTX780 MSI N770-2GD5/OC NVIDIA GeForce GTX770 MSI N760-2GD5 NVIDIA GeForce GTX760 MSI N750 TF 1GD5/OC NVIDIA GeForce GTX750 MSI GTX680-2GB/DDR5 NVIDIA GeForce GTX680 MSI N660Ti-PE-2GD5-OC/2G-DDR5 NVIDIA GeForce GTX660Ti MSI N680GTX Twin Frozr 2GD5/OC NVIDIA GeForce GTX680 GIGABYTE GV-N670OC-2GD NVIDIA GeForce GTX670 GIGABYTE GV-N650OC-1GI/1G-DDR5 NVIDIA GeForce GTX650 GIGABYTE GV-N590D5-3GD-B NVIDIA GeForce GTX590 MSI N580GTX-M2D15D5/1.5G NVIDIA GeForce GTX580 MSI N465GTX-M2D1G-B NVIDIA GeForce GTX465 LEADTEK GTX275/896M-DDR3 NVIDIA GeForce GTX275 LEADTEK PX8800 GTX TDH NVIDIA GeForce 8800GTX GIGABYTE GV-N26-896H-B -
NVIDIA Quadro RTX for V-Ray Next
NVIDIA QUADRO RTX V-RAY NEXT GPU Image courtesy of © Dabarti Studio, rendered with V-Ray GPU Quadro RTX Accelerates V-Ray Next GPU Rendering Solutions for V-Ray Next GPU V-Ray Next GPU taps into the power of NVIDIA® Quadro® NVIDIA Quadro® provides a wide range of RTX-enabled RTX™ to speed up production rendering with dedicated RT solutions for desktop, mobile, server-based rendering, and Cores for ray tracing and Tensor Cores for AI-accelerated virtual workstations with NVIDIA Quadro Virtual Data denoising.¹ With up to 18X faster rendering than CPU-based Center Workstation (Quadro vDWS) software.2 With up to 96 solutions and enhanced performance with NVIDIA NVLink™, gigabytes (GB) of GPU memory available,3 Quadro RTX V-Ray Next GPU with RTX support provides incredible provides the power you need for the largest professional performance improvements for your rendering workloads. graphics and rendering workloads. “ Accelerating artist productivity is always our top Benchmark: V-Ray Next GPU Rendering Performance Increase on Quadro RTX GPUs priority, so we’re quick to take advantage of the latest ray-tracing hardware breakthroughs. By Quadro RTX 6000 x2 1885 ™ Quadro RTX 6000 104 supporting NVIDIA RTX in V-Ray GPU, we’re Quadro RTX 4000 783 bringing our customers an exciting new boost in PU 1 0 2 4 6 8 10 12 14 16 18 20 their GPU production rendering speeds.” Relatve Performance – Phillip Miller, Vice President, Product Management, Chaos Group Desktop performance Tests run on 1x Xeon old 6154 3 Hz (37 Hz Turbo), 64 B DDR4 RAM Wn10x64 Drver verson 44128 Performance results may vary dependng on the scene NVIDIA Quadro professional graphics solutions are verified and recommended for the most demanding projects by Chaos Group. -
Nvidia Tesla P40 Gpu Accelerator
NVIDIA TESLA P40 GPU ACCELERATOR HIGH-PERFORMANCE VIRTUAL GRAPHICS AND COMPUTE NVIDIA redefined visual computing by giving designers, engineers, scientists, and graphic artists the power to take on the biggest visualization challenges with immersive, interactive, photorealistic environments. NVIDIA® Quadro® Virtual Data GPU 1 NVIDIA Pascal GPU Center Workstation (Quadro vDWS) takes advantage of NVIDIA® CUDA Cores 3,840 Tesla® GPUs to deliver virtual workstations from the data center. Memory Size 24 GB GDDR5 H.264 1080p30 streams 24 Architects, engineers, and designers are now liberated from Max vGPU instances 24 (1 GB Profile) their desks and can access applications and data anywhere. vGPU Profiles 1 GB, 2 GB, 3 GB, 4 GB, 6 GB, 8 GB, 12 GB, 24 GB ® ® The NVIDIA Tesla P40 GPU accelerator works with NVIDIA Form Factor PCIe 3.0 Dual Slot Quadro vDWS software and is the first system to combine an (rack servers) Power 250 W enterprise-grade visual computing platform for simulation, Thermal Passive HPC rendering, and design with virtual applications, desktops, and workstations. This gives organizations the freedom to virtualize both complex visualization and compute (CUDA and OpenCL) workloads. The NVIDIA® Tesla® P40 taps into the industry-leading NVIDIA Pascal™ architecture to deliver up to twice the professional graphics performance of the NVIDIA® Tesla® M60 (Refer to Performance Graph). With 24 GB of framebuffer and 24 NVENC encoder sessions, it supports 24 virtual desktops (1 GB profile) or 12 virtual workstations (2 GB profile), providing the best end-user scalability per GPU. This powerful GPU also supports eight different user profiles, so virtual GPU resources can be efficiently provisioned to meet the needs of the user. -
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. -
GPU-Based Deep Learning Inference
Whitepaper GPU-Based Deep Learning Inference: A Performance and Power Analysis November 2015 1 Contents Abstract ......................................................................................................................................................... 3 Introduction .................................................................................................................................................. 3 Inference versus Training .............................................................................................................................. 4 GPUs Excel at Neural Network Inference ..................................................................................................... 5 Inference Optimizations in Caffe and cuDNN 4 ........................................................................................ 5 Experimental Setup and Testing Methodology ........................................................................................ 7 Inference on Small and Large GPUs .......................................................................................................... 8 Conclusion ................................................................................................................................................... 10 References .................................................................................................................................................. 10 2 Abstract Deep learning methods are revolutionizing various areas of machine perception. On a -
Nvidia Cuda Getting Started Guide for Linux
NVIDIA CUDA GETTING STARTED GUIDE FOR LINUX DU-05347-001_v7.0 | March 2015 Installation and Verification on Linux Systems TABLE OF CONTENTS Chapter 1. Introduction.........................................................................................1 1.1. System Requirements.................................................................................... 1 1.1.1. x86 32-bit Support.................................................................................. 2 1.2. About This Document.................................................................................... 3 Chapter 2. Pre-installation Actions...........................................................................4 2.1. Verify You Have a CUDA-Capable GPU................................................................ 4 2.2. Verify You Have a Supported Version of Linux.......................................................4 2.3. Verify the System Has gcc Installed................................................................... 5 2.4. Choose an Installation Method......................................................................... 5 2.5. Download the NVIDIA CUDA Toolkit....................................................................6 2.6. Handle Conflicting Installation Methods.............................................................. 6 Chapter 3. Package Manager Installation....................................................................8 3.1. Overview..................................................................................................