NVIDIA Tegra 4 Family GPU Architecture
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Stochastic Modeling and Performance Analysis of Multimedia Socs
Stochastic Modeling and Performance Analysis of Multimedia SoCs Balaji Raman, Ayoub Nouri, Deepak Gangadharan, Marius Bozga, Ananda Basu, Mayur Maheshwari, Jérôme Milan, Axel Legay, Saddek Bensalem, Samarjit Chakraborty To cite this version: Balaji Raman, Ayoub Nouri, Deepak Gangadharan, Marius Bozga, Ananda Basu, et al.. Stochastic Modeling and Performance Analysis of Multimedia SoCs. SAMOS XIII - Embedded Computer Sys- tems: Architectures, Modeling, and Simulation, Jul 2013, Agios konstantinos, Samos Island, Greece. pp.145-154, 10.1109/SAMOS.2013.6621117. hal-00878094 HAL Id: hal-00878094 https://hal.archives-ouvertes.fr/hal-00878094 Submitted on 29 Oct 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Stochastic Modeling and Performance Analysis of Multimedia SoCs Balaji Raman1, Ayoub Nouri1, Deepak Gangadharan2, Marius Bozga1, Ananda Basu1, Mayur Maheshwari1, Jerome Milan3, Axel Legay4, Saddek Bensalem1, and Samarjit Chakraborty5 1 VERIMAG, France, 2 Technical Univeristy of Denmark, 3 Ecole Polytechnique, France, 4 INRIA Rennes, France, 5 Technical University of Munich, Germany. E-mail: [email protected] Abstract—Quality of video and audio output is a design-time to estimate buffer size for an acceptable output quality. The constraint for portable multimedia devices. -
An Emerging Architecture in Smart Phones
International Journal of Electronic Engineering and Computer Science Vol. 3, No. 2, 2018, pp. 29-38 http://www.aiscience.org/journal/ijeecs ARM Processor Architecture: An Emerging Architecture in Smart Phones Naseer Ahmad, Muhammad Waqas Boota * Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan Abstract ARM is a 32-bit RISC processor architecture. It is develop and licenses by British company ARM holdings. ARM holding does not manufacture and sell the CPU devices. ARM holding only licenses the processor architecture to interested parties. There are two main types of licences implementation licenses and architecture licenses. ARM processors have a unique combination of feature such as ARM core is very simple as compare to general purpose processors. ARM chip has several peripheral controller, a digital signal processor and ARM core. ARM processor consumes less power but provide the high performance. Now a day, ARM Cortex series is very popular in Smartphone devices. We will also see the important characteristics of cortex series. We discuss the ARM processor and system on a chip (SOC) which includes the Qualcomm, Snapdragon, nVidia Tegra, and Apple system on chips. In this paper, we discuss the features of ARM processor and Intel atom processor and see which processor is best. Finally, we will discuss the future of ARM processor in Smartphone devices. Keywords RISC, ISA, ARM Core, System on a Chip (SoC) Received: May 6, 2018 / Accepted: June 15, 2018 / Published online: July 26, 2018 @ 2018 The Authors. Published by American Institute of Science. This Open Access article is under the CC BY license. -
Comparative Study of Various Systems on Chips Embedded in Mobile Devices
Innovative Systems Design and Engineering www.iiste.org ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online) Vol.4, No.7, 2013 - National Conference on Emerging Trends in Electrical, Instrumentation & Communication Engineering Comparative Study of Various Systems on Chips Embedded in Mobile Devices Deepti Bansal(Assistant Professor) BVCOE, New Delhi Tel N: +919711341624 Email: [email protected] ABSTRACT Systems-on-chips (SoCs) are the latest incarnation of very large scale integration (VLSI) technology. A single integrated circuit can contain over 100 million transistors. Harnessing all this computing power requires designers to move beyond logic design into computer architecture, meet real-time deadlines, ensure low-power operation, and so on. These opportunities and challenges make SoC design an important field of research. So in the paper we will try to focus on the various aspects of SOC and the applications offered by it. Also the different parameters to be checked for functional verification like integration and complexity are described in brief. We will focus mainly on the applications of system on chip in mobile devices and then we will compare various mobile vendors in terms of different parameters like cost, memory, features, weight, and battery life, audio and video applications. A brief discussion on the upcoming technologies in SoC used in smart phones as announced by Intel, Microsoft, Texas etc. is also taken up. Keywords: System on Chip, Core Frame Architecture, Arm Processors, Smartphone. 1. Introduction: What Is SoC? We first need to define system-on-chip (SoC). A SoC is a complex integrated circuit that implements most or all of the functions of a complete electronic system. -
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 -
Overview of CPU Power Consumption and Management in Smartphones
Overview of CPU Power Consumption and Management in Smartphones Prof. Sasu Tarkoma University of Helsinki, Aalto University, Helsinki Institute for Information Technology Contents • Modern smartphone SoC and CPUs – The CPU: power states – Power management basics • Smartphone solutions – Linux CPU Frequency subsystem – Power models • Intra-device task offloading – Sensor hub – Heterogeneous multiprocessing • Computation offloading Smartphones • Smartphones have become hubs for applications and connecting with the Internet • Cloud has emerged as a backend for mobile applications • Mobile data and WiFi are the dominant protocols for connecting with Internet resources • The next generation solutions are addressing limitations of the current smartphones – Coordination of resource usage – Offloading in its many forms – Heterogeneous environment and the emergence of IoT / M2M / wearables Observations • Smartphone and mobile device hardware and software evolve rapidly • Multiple wireless protocols • Heterogeneous computing over multiple cores – Dedicated subsystems (sensor hubs) – Increasing number of sensing subsystems – Always-on sensing • Battery technology has not kept pace with the development • Software is not, in many cases, optimized • Difficult to balance between local versus distributed processing • Difficult to control traffic across interfaces Mobile Evolution 1995 2000 2005 2010 2015 Processor Single Single Single Dual-core Quad-core and beyond, auxiliary processors, sensor hubs Cellular 2G 2.5-3G 3.5G Transition 4G generation toward 4G Standard GSM GPRS HSPA HSPA, LTE LTE, LTE-A Downlink (Mb/ 0.01 0.1 1 10 100 s) Display pixels 4 16 64 256 1024 (x1000) Communicatio - - WiFi, WiFi, WiFi, Bluetooth LE, ns modules Bluetooth Bluetooth RFID Battery 1 2 3 4 5 capacity (Wh) Software (MB) 0.1 1 10 100 1000 Example Smartphone SoC Modem Subsystem Multicore Subsystem Multimedia Subsystem LTE Adreno World KRAIT CPU KRAIT CPU GPU Modem Audio, GPS,Wi-Fi, Video HW, BT,FM Accelerator L1 Cache L1 Cache s DSP DSP DSP L2 Cache Multim. -
M-Line BROCHURE
The Novasom Industries M-line was created for those advanced multimedia applications where the computing power and the presence of specific HW accelerators are needed as much as the advanced connectivity to various kinds of displays while maintaining the classic low-level industrial connectivity. Novasom Industries M11 series, based on the new Intel Apollo Lake x5 6th generation Atom CPU with Microsoft Windows 10 and UHD (4k) video capabilities, is perfect for the typical Kiosks & Digital-Signage applications. The M7 is based on the Rockchip RK3328, a 4X A53 processor and can drive UHD (4k) displays, has USB3 & 2, HDMI and supports Android OS. Complete SBC with immediate bootstrap Native Android & Linux support O.S. (M7, M8 & M9) M8 board runs Qualcomm Snapdragon 410E with Android and Windows 10 IoT and can be connected to FHD displays. Native Window 10 and Linux (M11) Embedded UPS manager with battery and Redundant The M9, based on the Rockchip RK3399 offers an android like Power Input experience and high side multimedia application with UHD, multiple video and camera input. HD Audio output and Optical SPDIF mPCIe interface slot (M9, M7 & M11) All the M-Line boards support Linux OS. Fluidity and no scratch on Heavy UHD play RASPMOOD form factor for M7, M8 and M9: dimensions, guaranteed mechanical holes, expansion pin on strip, connector Fully certified board, visit kind and position are similar to the famous Pi Family. www.novasomindustries.com for details So if you've started with a toy-board and want to use in an industrial proposal, we are ready. -
QTEE STOR, Is Shown in Figure 4
Qualcomm Snapdragon, Qualcomm Trusted Execution Environment, Qualcomm Secure Storage Solutions, Qualcomm Secure Processing Unit, Qualcomm Secure File System, and Qualcomm Fast Trusted Storage are products of Qualcomm Technologies, Inc. and/or its subsidiaries. Qualcomm and Snapdragon are trademarks of Qualcomm Incorporated, registered in the United States and other countries. Other products and brand names may be trademarks or registered trademarks of their respective owners. The contents of this document are provided on an “as-is” basis without warranty of any kind. Qualcomm Technologies, Inc. specifically disclaims the implied warranties of merchantability and fitness for a particular purpose. Qualcomm Technologies, Inc. 5775 Morehouse Drive San Diego, CA 92121 U.S.A. © 2019 Qualcomm Technologies, Inc. and/or its affiliated companies. All Rights Reserved. Overview .............................................................................................................................. 1 Acronyms ............................................................................................................................. 2 Limitation of pure software-based solutions ...................................................................... 3 Hardware building blocks .................................................................................................... 4 Qualcomm® Trusted Execution Environment ................................................................. 4 Hardware Crypto Engine ................................................................................................ -
Numerical Behavior of NVIDIA Tensor Cores
Numerical behavior of NVIDIA tensor cores Massimiliano Fasi1, Nicholas J. Higham2, Mantas Mikaitis2 and Srikara Pranesh2 1 School of Science and Technology, Örebro University, Örebro, Sweden 2 Department of Mathematics, University of Manchester, Manchester, UK ABSTRACT We explore the floating-point arithmetic implemented in the NVIDIA tensor cores, which are hardware accelerators for mixed-precision matrix multiplication available on the Volta, Turing, and Ampere microarchitectures. Using Volta V100, Turing T4, and Ampere A100 graphics cards, we determine what precision is used for the intermediate results, whether subnormal numbers are supported, what rounding mode is used, in which order the operations underlying the matrix multiplication are performed, and whether partial sums are normalized. These aspects are not documented by NVIDIA, and we gain insight by running carefully designed numerical experiments on these hardware units. Knowing the answers to these questions is important if one wishes to: (1) accurately simulate NVIDIA tensor cores on conventional hardware; (2) understand the differences between results produced by code that utilizes tensor cores and code that uses only IEEE 754-compliant arithmetic operations; and (3) build custom hardware whose behavior matches that of NVIDIA tensor cores. As part of this work we provide a test suite that can be easily adapted to test newer versions of the NVIDIA tensorcoresaswellassimilaracceleratorsfromothervendors,astheybecome available. Moreover, we identify a non-monotonicity issue -
Manycore GPU Architectures and Programming, Part 1
Lecture 19: Manycore GPU Architectures and Programming, Part 1 Concurrent and Mul=core Programming CSE 436/536, [email protected] www.secs.oakland.edu/~yan 1 Topics (Part 2) • Parallel architectures and hardware – Parallel computer architectures – Memory hierarchy and cache coherency • Manycore GPU architectures and programming – GPUs architectures – CUDA programming – Introduc?on to offloading model in OpenMP and OpenACC • Programming on large scale systems (Chapter 6) – MPI (point to point and collec=ves) – Introduc?on to PGAS languages, UPC and Chapel • Parallel algorithms (Chapter 8,9 &10) – Dense matrix, and sorng 2 Manycore GPU Architectures and Programming: Outline • Introduc?on – GPU architectures, GPGPUs, and CUDA • GPU Execuon model • CUDA Programming model • Working with Memory in CUDA – Global memory, shared and constant memory • Streams and concurrency • CUDA instruc?on intrinsic and library • Performance, profiling, debugging, and error handling • Direc?ve-based high-level programming model – OpenACC and OpenMP 3 Computer Graphics GPU: Graphics Processing Unit 4 Graphics Processing Unit (GPU) Image: h[p://www.ntu.edu.sg/home/ehchua/programming/opengl/CG_BasicsTheory.html 5 Graphics Processing Unit (GPU) • Enriching user visual experience • Delivering energy-efficient compung • Unlocking poten?als of complex apps • Enabling Deeper scien?fic discovery 6 What is GPU Today? • It is a processor op?mized for 2D/3D graphics, video, visual compu?ng, and display. • It is highly parallel, highly multhreaded mulprocessor op?mized for visual -
Numerical Behavior of NVIDIA Tensor Cores Fasi, Massimiliano And
Numerical Behavior of NVIDIA Tensor Cores Fasi, Massimiliano and Higham, Nicholas J. and Mikaitis, Mantas and Pranesh, Srikara 2020 MIMS EPrint: 2020.10 Manchester Institute for Mathematical Sciences School of Mathematics The University of Manchester Reports available from: http://eprints.maths.manchester.ac.uk/ And by contacting: The MIMS Secretary School of Mathematics The University of Manchester Manchester, M13 9PL, UK ISSN 1749-9097 Numerical Behavior of NVIDIA Tensor Cores Massimiliano Fasi*1, Nicholas J. Higham2, Mantas Mikaitis2, and Srikara Pranesh2 1School of Science and Technology, Orebro¨ University, Orebro,¨ Sweden 2Department of Mathematics, University of Manchester, Manchester, United Kingdom Corresponding author: Mantas Mikaitis2 Email address: [email protected] ABSTRACT We explore the floating-point arithmetic implemented in the NVIDIA tensor cores, which are hardware accelerators for mixed-precision matrix multiplication available on the Volta, Turing, and Ampere microarchitectures. Using Volta V100, Turing T4 and Ampere A100 graphics cards, we determine what precision is used for the intermediate results, whether subnormal numbers are supported, what rounding mode is used, in which order the operations underlying the matrix multiplication are performed, and whether partial sums are normalized. These aspects are not documented by NVIDIA, and we gain insight by running carefully designed numerical experiments on these hardware units. Knowing the answers to these questions is important if one wishes to: 1) accurately simulate NVIDIA tensor cores on conventional hardware; 2) understand the differences between results produced by code that utilizes tensor cores and code that uses only IEEE 754-compliant arithmetic operations; and 3) build custom hardware whose behavior matches that of NVIDIA tensor cores. -
NVIDIA's Opengl Functionality
NVIDIANVIDIA ’’ss OpenGLOpenGL FunctionalityFunctionality Session 2127 | Room A5 | Monday, September, 20th, 16:00 - 17:20 San Jose Convention Center, San Jose, California Mark J. Kilgard • Principal System Software Engineer – OpenGL driver – Cg (“C for graphics”) shading language • OpenGL Utility Toolkit (GLUT) implementer • Author of OpenGL for the X Window System • Co-author of Cg Tutorial Outline • OpenGL’s importance to NVIDIA • OpenGL 3.3 and 4.0 • OpenGL 4.1 • Loose ends: deprecation, Cg, further extensions OpenGL Leverage Cg Parallel Nsight SceniX CompleX OptiX Example of Hybrid Rendering with OptiX OpenGL (Rasterization) OptiX (Ray tracing) Parallel Nsight Provides OpenGL Profiling Configure Application Trace Settings Parallel Nsight Provides OpenGL Profiling Magnified trace options shows specific OpenGL (and Cg) tracing options Parallel Nsight Provides OpenGL Profiling Parallel Nsight Provides OpenGL Profiling Trace of mix of OpenGL and CUDA shows glFinish & OpenGL draw calls OpenGL In Every NVIDIA Business OpenGL on Quadro – World class OpenGL 4 drivers – 18 years of uninterrupted API compatibility – Workstation application certifications – Workstation application profiles – Display list optimizations – Fast antialiased lines – Largest memory configurations: 6 gigabytes – GPU affinity – Enhanced interop with CUDA and multi-GPU OpenGL – Advanced multi-GPU rendering – Overlays – Genlock – Unified Back Buffer for less framebuffer memory usage – Cross-platform • Windows XP, Vista, Win7, Linux, Mac, FreeBSD, Solaris – SLI Mosaic – -
Arxiv:1910.06663V1 [Cs.PF] 15 Oct 2019
AI Benchmark: All About Deep Learning on Smartphones in 2019 Andrey Ignatov Radu Timofte Andrei Kulik ETH Zurich ETH Zurich Google Research [email protected] [email protected] [email protected] Seungsoo Yang Ke Wang Felix Baum Max Wu Samsung, Inc. Huawei, Inc. Qualcomm, Inc. MediaTek, Inc. [email protected] [email protected] [email protected] [email protected] Lirong Xu Luc Van Gool∗ Unisoc, Inc. ETH Zurich [email protected] [email protected] Abstract compact models as they were running at best on devices with a single-core 600 MHz Arm CPU and 8-128 MB of The performance of mobile AI accelerators has been evolv- RAM. The situation changed after 2010, when mobile de- ing rapidly in the past two years, nearly doubling with each vices started to get multi-core processors, as well as power- new generation of SoCs. The current 4th generation of mo- ful GPUs, DSPs and NPUs, well suitable for machine and bile NPUs is already approaching the results of CUDA- deep learning tasks. At the same time, there was a fast de- compatible Nvidia graphics cards presented not long ago, velopment of the deep learning field, with numerous novel which together with the increased capabilities of mobile approaches and models that were achieving a fundamentally deep learning frameworks makes it possible to run com- new level of performance for many practical tasks, such as plex and deep AI models on mobile devices. In this pa- image classification, photo and speech processing, neural per, we evaluate the performance and compare the results of language understanding, etc.