DOCSLIB.ORG

Evga Tegra Note 7 Android 5.1 Ota 3.0 Download

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Evga Tegra Note 7 Android 5.1 Ota 3.0 Download evga tegra note 7 android 5.1 ota 3.0 download Fixing Lollipop 5.1 on the EVGA Tegra Note 7? When my wife and I first updated our EVGA Tegra Note 7 to Lollipop we found this once very-fast, cool, and battery-sipping tablet became unbearably laggy, hot, and power-hungry. We found this odd because we own the Shield Portable with the same SoC and 5.1 Lollipop runs great on that device. This is just a guess, but we think the problem of accumulated system or user data slows the little tablet down because the Note 7 only has half the RAM (1GB). We tried to resolve our issues by performing a factory reset. Unfortunately this did not work. However, we were not beaten yet. Read on to see how we got our little tablet to work well with Lollipop 5.1. Disclaimers. Important : This solution is only known to work for my EVGA Tegra Note 7 WiFi model, TegraNote-P1640 as shown in Settings > About tablet > Model number . Attempting the solution on a different model could likely brick the device. There’s even a chance attempting the solution on devices with the same model number could get bricked. Proceed at your own risk. I also encourage readers to review the excellent XDA ROMS work from William Rogers for alternate solutions that may be a better choice for their model or situation. The solution. Our initial intent was to download the OTA 3.0 GMS recovery image from Nvidia downloads page and install it. While there is a link for the OTA 3.0.0 ROM, the link is broken, and it is for the P1988/P1988W model. Instead we decided to load the “OTA 2.5” image and then try updating to Lollipop using OTA. And it worked! Once we had finished the tablet runs much better than the previous Lollipop install. As an added bonus, if we want to revert to KitKat 4.4 (“OTA 2.5”) - which is an excellent ROM - we can do so in 10 minutes or so. The entire procedure took less than a half hour. Here are the steps we took: We unlocked the boot loader using the instructions. We then installed the NVIDIA Tegra NOTE7 GMS Recovery OS Image (OTA 2.5) from the Nvidia downloads page per the instructions. We used the the root account on Linux to avoid any device access permissions issues. Once we flashed all the images using the flash-all.sh script, we had to select continue on the tablet’s bootloader menu and press the power button to continue with the update. When we signed-in for the first time we disabled BACKUP & RESTORE to minimize update time. We also disabled the For improved accuracy . option to maximize battery life. Once our initial sign-in was complete, we allowed automatic updates to proceed. It took about 15 minutes for all apps to update. We had to approve 5 of them. Because we had disabled BACKUP & RESTORE earlier, we probably saved time by avoiding downloading and updating data and applications. Immediately after all updates were applied, we clicked on the OTA 3.0 update in the top-left corner of the screen and pressed Restart and Install . After the OTA was complete - in other words, Android 5.1 had been installed - we went to Settings > Backup & Reset and enabled Back up my data and Automatic restore . We also created a power-control shortcut using these instructions. We optimized all apps, and we turned off WiFi during sleep. This is important to maximize battery life and reduce heat ! We found that adding a second account reduced performance noticeably. This is probably due to the limited RAM. Just to be safe, we repeated this procedure using my wife’s account as the primary and only account. We have found the upgrade to be very much improved. The tablet is responsive and cool. The battery life is good again, and it charges completely in under 2 hours (using a Shield charger). Parting thoughts. One thing we really appreciate about Nvidia is that they usually provide some of the best Android support outside of the Google Nexus program and Motorola. Our family have no carrier contracts and we own all of our Android phones and tablets - Nvidia, Nexus, Moto, ASUS, and EVGA devices, in that order. So we were really jazzed about the Tegra Note 7 getting the Lollipop upgrade because it had been overlooked for the last year. It’s too bad that we had to wrestle with the problem, which we suspect affects the early adopters more than recent purchasers - there is some evidence that more recent Tegra Notes perform very well after the OTA to Lollipop. We wish Nvidia would do a bit more to own this problem, because as the above experience shows, they are so close to having a really great update for everyone. Downgrade to kitkat from lollipop. Lollipop is good but you need to tweak it to get it back to earth. * Make sure your tablet is OEM unlocked. Then flash a sophisticated recover image version like TWRP. This will allow root-mode enabling via a patch. * Put the tablet into fastboot mode and flash RootJunky's shared Lollipop restore image instead of the upgrade over the air (OTA). Good idea to erase particular partition before flashing. * Boot to the recovery stage. Install latest zip SuperSu.zip using sideloader or having it already on a SD. This will enable root mode. * Start system. Remove Apps from system partition unneeded, or that can be moved or installed to user directory instead. This frees up vital space. Example Lollipop only leaves a few megabytes left in the system partition after installation. Consider using Link2SD. Example get Google+, Google Play, Google Magazine, etc into the user partition and any other big App off your system parition. The system partition should only have important apps needed to be retained if a factory wipe is enacted. * Then go through all your system settings make sure you tweak them to suit both power saving and your own desires. * Use a startup tool like ES Task Manager to remove apps starting that don't need to be running from system boot. * Install all Google Apps wanted. This can help to configure the system how it's meant to be. * The system should now run smooth and use little battery. ---------- Post added at 10:15 PM ---------- Previous post was at 10:14 PM ---------- NVIDIA CodeWorks for Android. Setting up an Android development environment can be a complex and frustrating experience. NVIDIA CodeWorks for Android simplifies this for all Android developers with a single installer that manages this complexity for you. NVIDIA CodeWorks for Android is a professional grade solution providing a comprehensive set of GPU and CPU tools with debugging, profiling and system trace capabilities and accompanying SDKs and libraries for Android development - not only on Tegra powered devices. NVIDIA CodeWorks for Android installs all software tools required to develop for the latest SHIELD Android OS and reduces the complex process of configuring an Android development system down to a single click. CodeWorks for Android is offered free of charge, but requires NVIDIA Registered Developer Program membership. Available for the NVIDIA SHIELD TV and Tablet! This suite of developer tools will configure a development environment that will work with most Android devices (Compatible devices). CodeWorks for Android is available for Windows, OSX, Ubuntu, Linux 64-bit platforms. "With functionality that extends beyond Tegra, NVIDIA CodeWorks for Android is a valuable part of EA's mobile game development tools. CodeWorks for Android helps us bring players incredible mobile experiences on a variety of Android devices and chipsets," Lewis Strudwick , Studio Technology Director at EA Melbourne. "Epic's use of Nsight Tegra has been crucial for the efficient development of Unreal Engine 4's Android features. We're delighted to see CodeWorks for Android extend support beyond Tegra so Unreal Engine developers can remain in their favorite authoring tools during all development scenarios. NVIDIA's tool advancements are refining Android to a level matching game console development." Niklas Smedberg , Senior Graphics Programmer at Epic Games. Release (1R8) New to this release are updated developer tools, the latest Android NDK, SDK, and Build/Platform Tools for Android development. Highlights: Support for Android NDK64 r18b Support for API 28 Various performance improvements and bug fixes Tegra Graphics Debugger is now Nsight Graphics Visit the product page for more info NVIDIA System Profiler is now Nsight System Visit the product page for more info. Support. To provide feedback, request additional features or report support issues, please use the NVIDIA Developer Forums. Features. Android Development: Android SDK r26.1.1 Android API 28 Android NDK64 r18b Android Build Tools r28.03 Android Platform Tools r28.0.3 Android Support Library r23.2.1 Android Support Repository Library r47 Google USB Driver r11 JDK 1.8.0_77 or OpenJDK 8 Gradle 4.4.1. GameWorks Libraries and Tools: Nsight Tegra 3.6, Visual Studio Edition Nsight Graphics 2019.3 Nsight Systems 2019.4. Compatible Devices. Android ABI (ARM v7, ARM v8, x86, x64) support, including but not limited to: NVIDIA® SHIELD™ Android TV Support. System Requirements. Supported Operating Systems: Windows 7/10 (64-bit) Mac OS X 10.14 Ubuntu Linux x64 (v16.04 and v18.04) Developer Webinars. Instructional Videos and Webinars can be found here. To access older versions of CodeWorks for Android (formerly AndroidWorks/Tegra Android Development Pack), please visit CodeWorks for Android Archive. EVGA Tegra Note 7 Tablet Launched. The EVGA Tegra Note 7 tablet was announced earlier this month, this new tablet is now available in the US and it retails for $200, and comes with Android 4.2.2 Jelly Bean.
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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 .
    [Show full text]
  • 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.
    [Show full text]
  • Low-Power Ultra-Small Edge AI Accelerators for Image Recog- Nition with Convolution Neural Networks: Analysis and Future Directions
    Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 16 July 2021 doi:10.20944/preprints202107.0375.v1 Review Low-power Ultra-small Edge AI Accelerators for Image Recog- nition with Convolution Neural Networks: Analysis and Future Directions Weison Lin 1, *, Adewale Adetomi 1 and Tughrul Arslan 1 1 Institute for Integrated Micro and Nano Systems, University of Edinburgh, Edinburgh EH9 3FF, UK; [email protected]; [email protected] * Correspondence: [email protected] Abstract: Edge AI accelerators have been emerging as a solution for near customers’ applications in areas such as unmanned aerial vehicles (UAVs), image recognition sensors, wearable devices, ro- botics, and remote sensing satellites. These applications not only require meeting performance tar- gets but also meeting strict reliability and resilience constraints due to operations in harsh and hos- tile environments. Numerous research articles have been proposed, but not all of these include full specifications. Most of these tend to compare their architecture with other existing CPUs, GPUs, or other reference research. This implies that the performance results of the articles are not compre- hensive. Thus, this work lists the three key features in the specifications such as computation ability, power consumption, and the area size of prior art edge AI accelerators and the CGRA accelerators during the past few years to define and evaluate the low power ultra-small edge AI accelerators. We introduce the actual evaluation results showing the trend in edge AI accelerator design about key performance metrics to guide designers on the actual performance of existing edge AI acceler- ators’ capability and provide future design directions and trends for other applications with chal- lenging constraints.
    [Show full text]
  • 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 ............................................................................
    [Show full text]
  • NVIDIA Tegra 4 Family CPU Architecture 4-PLUS-1 Quad Core
    Whitepaper NVIDIA Tegra 4 Family CPU Architecture 4-PLUS-1 Quad core 1 Table of Contents ...................................................................................................................................................................... 1 Introduction .............................................................................................................................................. 3 NVIDIA Tegra 4 Family of Mobile Processors ............................................................................................ 3 Benchmarking CPU Performance .............................................................................................................. 4 Tegra 4 Family CPUs Architected for High Performance and Power Efficiency ......................................... 6 Wider Issue Execution Units for Higher Throughput ............................................................................ 6 Better Memory Level Parallelism from a Larger Instruction Window for Out-of-Order Execution ...... 7 Fast Load-To-Use Logic allows larger L1 Data Cache ............................................................................. 8 Enhanced branch prediction for higher efficiency .............................................................................. 10 Advanced Prefetcher for higher MLP and lower latency .................................................................... 10 Large Unified L2 Cache .......................................................................................................................
    [Show full text]
  • 130 Demystifying Arm Trustzone: a Comprehensive Survey
    Demystifying Arm TrustZone: A Comprehensive Survey SANDRO PINTO, Centro Algoritmi, Universidade do Minho NUNO SANTOS, INESC-ID, Instituto Superior Técnico, Universidade de Lisboa The world is undergoing an unprecedented technological transformation, evolving into a state where ubiq- uitous Internet-enabled “things” will be able to generate and share large amounts of security- and privacy- sensitive data. To cope with the security threats that are thus foreseeable, system designers can find in Arm TrustZone hardware technology a most valuable resource. TrustZone is a System-on-Chip and CPU system- wide security solution, available on today’s Arm application processors and present in the new generation Arm microcontrollers, which are expected to dominate the market of smart “things.” Although this technol- ogy has remained relatively underground since its inception in 2004, over the past years, numerous initiatives have significantly advanced the state of the art involving Arm TrustZone. Motivated by this revival ofinter- est, this paper presents an in-depth study of TrustZone technology. We provide a comprehensive survey of relevant work from academia and industry, presenting existing systems into two main areas, namely, Trusted Execution Environments and hardware-assisted virtualization. Furthermore, we analyze the most relevant weaknesses of existing systems and propose new research directions within the realm of tiniest devices and the Internet of Things, which we believe to have potential to yield high-impact contributions in the future. CCS Concepts: • Computer systems organization → Embedded and cyber-physical systems;•Secu- rity and privacy → Systems security; Security in hardware; Software and application security; Additional Key Words and Phrases: TrustZone, security, virtualization, TEE, survey, Arm ACM Reference format: Sandro Pinto and Nuno Santos.
    [Show full text]
  • Low-Power Ultra-Small Edge AI Accelerators for Image Recognition with Convolution Neural Networks: Analysis and Future Directions
    electronics Review Low-Power Ultra-Small Edge AI Accelerators for Image Recognition with Convolution Neural Networks: Analysis and Future Directions Weison Lin *, Adewale Adetomi and Tughrul Arslan Institute for Integrated Micro and Nano Systems, University of Edinburgh, Edinburgh EH9 3FF, UK; [email protected] (A.A.); [email protected] (T.A.) * Correspondence: [email protected] Abstract: Edge AI accelerators have been emerging as a solution for near customers’ applications in areas such as unmanned aerial vehicles (UAVs), image recognition sensors, wearable devices, robotics, and remote sensing satellites. These applications require meeting performance targets and resilience constraints due to the limited device area and hostile environments for operation. Numerous research articles have proposed the edge AI accelerator for satisfying the applications, but not all include full specifications. Most of them tend to compare the architecture with other existing CPUs, GPUs, or other reference research, which implies that the performance exposé of the articles are not comprehensive. Thus, this work lists the essential specifications of prior art edge AI accelerators and the CGRA accelerators during the past few years to define and evaluate the low power ultra-small edge AI accelerators. The actual performance, implementation, and productized examples of edge AI accelerators are released in this paper. We introduce the evaluation results showing the edge AI accelerator design trend about key performance metrics to guide designers. Citation: Lin, W.; Adetomi, A.; Last but not least, we give out the prospect of developing edge AI’s existing and future directions Arslan, T. Low-Power Ultra-Small Edge AI Accelerators for Image and trends, which will involve other technologies for future challenging constraints.
    [Show full text]
  • Tegra NOTE 7
    Tegra NOTE 7 User Guide 1 Contents About Tegra NOTE 7 ............................................................................................................................................. 4 What’s in the Box ............................................................................................................................................... 4 Buttons and Connectors ...................................................................................................................................... 5 Charging Your Tegra NOTE 7 ............................................................................................................................. 6 Inserting an SD Card ........................................................................................................................................... 7 Get Started ............................................................................................................................................................ 8 Power On ........................................................................................................................................................... 8 Do I Need a Google Account? ......................................................................................................................... 8 Let Your Fingers do the Work ............................................................................................................................. 9 About DirectStylus ...........................................................................................................................................
    [Show full text]
  • Планшеты Планшет Lenovo Lepad A2207 7" Планшет Обладающий
    планшеты, каталог компания: Украина, г. Днепропетровск Телефон / Факс указан на сайте tetis.com.ua в соответствующей группе более детальную информацию о предоставленых моделях вы можете получить на сайте http://tetis.dp.ua наименование описание стоимость гарантия планшеты Lenovo A2207 Lenovo LePad A2207 7" планшет обладающий функциями планшет GPS с полноценным 3G модулем (2 сим возможность осуществлять мобильные звонки). Работает планшет на двухядерном процессоре MTK8377 Dual core A9 по 1 Ггц на каждое из ядер. 512 ОЗУ и 4 ПЗУ (постоянная память) 0 12 по желанию можно расширить память использую microSD карту до 32 Гб включительно. Дисплей е... Ainol Novo 7 Elf Ainol Novo 7 Elf модель оснащение мощным процессором Планшет AllWinnerA10 на ядре ARM Cortex ™A8 с завышенной частотой до 1,2 ГГц, работает в связке с графическим ядром Mali-400, что положительно влияет на производительность 3D графики. 0 12 Ainol Novo 7 Elf оснащен качественным емкостным 7-дюймовым экраном. 1024 х 600 пикселей разрешение и контрастная м... Ainol Novo 7 Fire / Flame/ BurningХит среде 7" планшетов - Ainol Novo 7 Flame. Планшет Этот девайc просто не может не привлечь внимание. Первое что бросается в глаза это стильный дизайн (Фото обзор смотрите ниже), Flame выполнен в металлическом корпусе с ребрами на задней части девайся, по бокам 0 0 металлические вставки с более светлого алюминия. В общем новинка смотрится очень богато. Этот... Ainol Novo 7 Aurora 2 Лидирующая компания Ainol Novo никогда не делала Планшетный ПК плохую продукцию, так произошло и на этот раз, на конвейер вышел новый девайс под названием Aurora II. Эта модель в первою очередь интересна своими техническими данными.
    [Show full text]
  • Manufacturer Device Model Consumers Count Apple Iphone
    Manufacturer Device Model Consumers Count Apple iPhone 24,666,239 Apple iPad 13,155,907 samsung SM-J500M 1,079,744 Apple iPod touch 1,070,538 samsung SM-G531H 1,043,553 samsung SM-G935F 1,026,327 samsung SM-T113 894,096 samsung SM-J700M 888,680 motorola MotoG3 860,116 samsung SM-J700F 847,315 samsung SM-G920F 834,655 samsung SM-G900F 827,050 samsung SM-G610F 786,659 HUAWEI ALE-L21 783,180 OPPO A37f 701,488 samsung SM-G955U 699,321 samsung SM-G930F 685,195 samsung SM-J510FN 673,415 samsung SM-G950U 654,635 samsung SM-G530H 651,695 samsung SM-J710F 647,723 motorola Moto G (4) 640,091 samsung SM-T110 627,013 samsung SM-J200G 611,728 OPPO A1601 588,226 samsung SM-G925F 571,858 samsung SM-G930V 557,813 samsung SM-A510F 533,209 ZTE Z981 532,290 samsung GT-I9300 516,580 samsung SM-J320FN 511,109 Xiaomi Redmi Note 4 507,119 samsung GT-I9505 504,325 samsung GT-I9060I 488,253 samsung SM-J120H 472,748 samsung SM-G900V 458,996 Xiaomi Redmi Note 3 435,822 samsung SM-A310F 435,163 samsung SM-T560 435,042 motorola XT1069 433,667 motorola Moto G Play 422,147 LGE LG-K430 406,009 samsung GT-I9500 392,674 Xiaomi Redmi 3S 388,092 samsung SM-J700H 384,922 samsung SM-G532G 384,884 samsung SM-N9005 382,982 samsung SM-G531F 382,728 motorola XT1033 380,899 Generic Android 7.0 374,405 motorola XT1068 373,075 samsung SM-J500FN 372,029 samsung SM-J320M 366,049 samsung SM-J105B 351,985 samsung SM-T230 348,374 samsung SM-T280 347,350 samsung SM-T113NU 341,313 samsung SM-T350 338,525 samsung SM-G935V 337,090 samsung SM-J500F 332,972 samsung SM-J320F 329,165 motorola
    [Show full text]
  • On Energy-Efficient Offloading in Mobile Cloud for Real-Time Video
    170 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 27, NO. 1, JANUARY 2017 On Energy-Efficient Offloading in Mobile Cloud for Real-Time Video Applications Lei Zhang, Student Member, IEEE,DiFu,Student Member, IEEE, Jiangchuan Liu, Senior Member, IEEE, Edith Cheuk-Han Ngai, Senior Member, IEEE, and Wenwu Zhu, Fellow, IEEE Abstract— Batteries of modern mobile devices remain severely nearly 10-fold between 2014 and 2019, reaching 24.3 EB limited in capacity, which makes energy consumption a key per month by 2019, a majority of which will be video concern for mobile applications, particularly for the computation- related [1]. Despite the fast development of the technolo- intensive video applications. Mobile devices can save energy by offloading computation tasks to the cloud, yet the energy gies and the effort toward unifying hand-held and desktop gain must exceed the additional communication cost for cloud computers (e.g., through Windows 8/10, iOS/MacOS, and migration to be beneficial. The situation is further complicated Android/ChromeOS), it remains widely agreed that mobile by real-time video applications that have stringent delay and terminals will not completely replace laptop and desktop bandwidth constraints. In this paper, we closely examine the computers in the near future. Migrating popular PC software performance and energy efficiency of representative mobile cloud applications under dynamic wireless network channels to mobile platforms or developing similar new software is still and state-of-the-art mobile platforms. We identify the unique confined to the limited computation capability of the mobile challenges of and opportunities for offloading real-time video devices, as well as their unique and lightweight operating applications and develop a generic model for energy-efficient systems and hardware architectures.
    [Show full text]