Hardware Accelerated 2D Rendering for Android
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Enabling Hardware Accelerated Video Decode on Intel® Atom™ Processor D2000 and N2000 Series Under Fedora 16
Enabling Hardware Accelerated Video Decode on Intel® Atom™ Processor D2000 and N2000 Series under Fedora 16 Application Note October 2012 Order Number: 509577-003US INFORMATIONLegal Lines and Disclaimers IN THIS DOCUMENT IS PROVIDED IN CONNECTION WITH INTEL PRODUCTS. NO LICENSE, EXPRESS OR IMPLIED, BY ESTOPPEL OR OTHERWISE, TO ANY INTELLECTUAL PROPERTY RIGHTS IS GRANTED BY THIS DOCUMENT. EXCEPT AS PROVIDED IN INTEL'S TERMS AND CONDITIONS OF SALE FOR SUCH PRODUCTS, INTEL ASSUMES NO LIABILITY WHATSOEVER AND INTEL DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY, RELATING TO SALE AND/OR USE OF INTEL PRODUCTS INCLUDING LIABILITY OR WARRANTIES RELATING TO FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR INFRINGEMENT OF ANY PATENT, COPYRIGHT OR OTHER INTELLECTUAL PROPERTY RIGHT. A “Mission Critical Application” is any application in which failure of the Intel Product could result, directly or indirectly, in personal injury or death. SHOULD YOU PURCHASE OR USE INTEL'S PRODUCTS FOR ANY SUCH MISSION CRITICAL APPLICATION, YOU SHALL INDEMNIFY AND HOLD INTEL AND ITS SUBSIDIARIES, SUBCONTRACTORS AND AFFILIATES, AND THE DIRECTORS, OFFICERS, AND EMPLOYEES OF EACH, HARMLESS AGAINST ALL CLAIMS COSTS, DAMAGES, AND EXPENSES AND REASONABLE ATTORNEYS' FEES ARISING OUT OF, DIRECTLY OR INDIRECTLY, ANY CLAIM OF PRODUCT LIABILITY, PERSONAL INJURY, OR DEATH ARISING IN ANY WAY OUT OF SUCH MISSION CRITICAL APPLICATION, WHETHER OR NOT INTEL OR ITS SUBCONTRACTOR WAS NEGLIGENT IN THE DESIGN, MANUFACTURE, OR WARNING OF THE INTEL PRODUCT OR ANY OF ITS PARTS. Intel may make changes to specifications and product descriptions at any time, without notice. Designers must not rely on the absence or characteristics of any features or instructions marked “reserved” or “undefined”. -
Moore's Law Motivation
Motivation: Moore’s Law • Every two years: – Double the number of transistors CprE 488 – Embedded Systems Design – Build higher performance general-purpose processors Lecture 8 – Hardware Acceleration • Make the transistors available to the masses • Increase performance (1.8×↑) Joseph Zambreno • Lower the cost of computing (1.8×↓) Electrical and Computer Engineering Iowa State University • Sounds great, what’s the catch? www.ece.iastate.edu/~zambreno rcl.ece.iastate.edu Gordon Moore First, solve the problem. Then, write the code. – John Johnson Zambreno, Spring 2017 © ISU CprE 488 (Hardware Acceleration) Lect-08.2 Motivation: Moore’s Law (cont.) Motivation: Dennard Scaling • The “catch” – powering the transistors without • As transistors get smaller their power density stays melting the chip! constant 10,000,000,000 2,200,000,000 Transistor: 2D Voltage-Controlled Switch 1,000,000,000 Chip Transistor Dimensions 100,000,000 Count Voltage 10,000,000 ×0.7 1,000,000 Doping Concentrations 100,000 Robert Dennard 10,000 2300 Area 0.5×↓ 1,000 130W 100 Capacitance 0.7×↓ 10 0.5W 1 Frequency 1.4×↑ 0 Power = Capacitance × Frequency × Voltage2 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Power 0.5×↓ Zambreno, Spring 2017 © ISU CprE 488 (Hardware Acceleration) Lect-08.3 Zambreno, Spring 2017 © ISU CprE 488 (Hardware Acceleration) Lect-08.4 Motivation Dennard Scaling (cont.) Motivation: Dark Silicon • In mid 2000s, Dennard scaling “broke” • Dark silicon – the fraction of transistors that need to be powered off at all times (due to power + thermal -
Use of Hardware Acceleration on PC As of March, 2020
Use of hardware acceleration on PC As of March, 2020 When displaying images of the camera using hardware acceleration of the PC, required the specifications of PC as folow. "The PC is equipped with specific hardware (CPU*1, GPU, etc.), and device drivers corresponding to those hardware are installed properly." ® *1: The CPU of the PC must support QSV(Intel Quick Sync Video). This information provides test results conducted under our environment and does not guarantee under all conditions. Please note that we used Internet Explorer for downloading at the following description and its procedures may differ if you use another browser software. Images may not be displayed, may be delayed or some of the images may be corrupted as follow, depends on usage conditions, performance and network environment of the PC. - When multiple windows are opened and operated on the PC - When sufficient bandwidth cannot be secured in your network environment - When the network driver of your computer is old (the latest version is recommended) *When the image is not displayed or the image is distorted, it may be improved by refreshing the browser. Applicable models: i-PRO EXTREME series models*2 (H.265/H.264 compatible models and H.265 compatible models) , i-PRO SmartHD series models*2 (H.264 compatible models) *2: You can find if it supports hardware acceleration by existence a setting item of [Drawing method] and [Decoding Options] in [Viewer software (nwcv4Ssetup.exe/ nwcv5Ssetup.exe)] of the setting menu of the camera. Items of the function that utilize PC hardware acceleration (Click the items as follow to go to each setting procedures.) 1.About checking if it supports "QSV" 2. -
NVIDIA Ampere GA102 GPU Architecture Whitepaper
NVIDIA AMPERE GA102 GPU ARCHITECTURE Second-Generation RTX Updated with NVIDIA RTX A6000 and NVIDIA A40 Information V2.0 Table of Contents Introduction 5 GA102 Key Features 7 2x FP32 Processing 7 Second-Generation RT Core 7 Third-Generation Tensor Cores 8 GDDR6X and GDDR6 Memory 8 Third-Generation NVLink® 8 PCIe Gen 4 9 Ampere GPU Architecture In-Depth 10 GPC, TPC, and SM High-Level Architecture 10 ROP Optimizations 11 GA10x SM Architecture 11 2x FP32 Throughput 12 Larger and Faster Unified Shared Memory and L1 Data Cache 13 Performance Per Watt 16 Second-Generation Ray Tracing Engine in GA10x GPUs 17 Ampere Architecture RTX Processors in Action 19 GA10x GPU Hardware Acceleration for Ray-Traced Motion Blur 20 Third-Generation Tensor Cores in GA10x GPUs 24 Comparison of Turing vs GA10x GPU Tensor Cores 24 NVIDIA Ampere Architecture Tensor Cores Support New DL Data Types 26 Fine-Grained Structured Sparsity 26 NVIDIA DLSS 8K 28 GDDR6X Memory 30 RTX IO 32 Introducing NVIDIA RTX IO 33 How NVIDIA RTX IO Works 34 Display and Video Engine 38 DisplayPort 1.4a with DSC 1.2a 38 HDMI 2.1 with DSC 1.2a 38 Fifth Generation NVDEC - Hardware-Accelerated Video Decoding 39 AV1 Hardware Decode 40 Seventh Generation NVENC - Hardware-Accelerated Video Encoding 40 NVIDIA Ampere GA102 GPU Architecture ii Conclusion 42 Appendix A - Additional GeForce GA10x GPU Specifications 44 GeForce RTX 3090 44 GeForce RTX 3070 46 Appendix B - New Memory Error Detection and Replay (EDR) Technology 49 Appendix C - RTX A6000 GPU Perf ormance 50 List of Figures Figure 1. -
Research Into Computer Hardware Acceleration of Data Reduction and SVMS
University of Rhode Island DigitalCommons@URI Open Access Dissertations 2016 Research Into Computer Hardware Acceleration of Data Reduction and SVMS Jason Kane University of Rhode Island, [email protected] Follow this and additional works at: https://digitalcommons.uri.edu/oa_diss Recommended Citation Kane, Jason, "Research Into Computer Hardware Acceleration of Data Reduction and SVMS" (2016). Open Access Dissertations. Paper 428. https://digitalcommons.uri.edu/oa_diss/428 This Dissertation is brought to you for free and open access by DigitalCommons@URI. It has been accepted for inclusion in Open Access Dissertations by an authorized administrator of DigitalCommons@URI. For more information, please contact [email protected]. RESEARCH INTO COMPUTER HARDWARE ACCELERATION OF DATA REDUCTION AND SVMS BY JASON KANE A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN COMPUTER ENGINEERING UNIVERSITY OF RHODE ISLAND 2016 DOCTOR OF PHILOSOPHY DISSERTATION OF JASON KANE APPROVED: Dissertation Committee: Major Professor Qing Yang Haibo He Joan Peckham Nasser H. Zawia DEAN OF THE GRADUATE SCHOOL UNIVERSITY OF RHODE ISLAND 2016 ABSTRACT Yearly increases in computer performance have diminished as of late, mostly due to the inability of transistors, the building blocks of computers, to deliver the same rate of performance seen in the 1980’s and 90’s. Shifting away from traditional CPU design, accelerator architectures have been shown to offer a potentially untapped solution. These architectures implement unique, custom hardware to increase the speed of certain tasking, such as graphics processing. The studies undertaken for this dissertation examine the ability of unique accelerator hardware to provide improved power and speed performance over traditional means, with an emphasis on classification tasking. -
Scope and Issues in Alpha Compositing Technology
International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2763 Issue 12, Volume 2 (December 2015) www.ijirae.com Scope and Issues in Alpha Compositing Technology Sudipta Maji Asoke Nath M.Sc. Computer Science Department Of Computer Science Department Of Computer Science St. Xavier's College St. Xavier's College Abstract— Alpha compositing is the process of combining an image with a background to create the appearance of partial transparency. The combining operation takes advantage of an alpha channel, which basically determines how much of a source pixel's color information covers a destination pixel's color information. In this documentation, the authors discuss different types of alpha blending modes which are used to achieve this partial transparency. Alpha compositing is a process which is used to combine two images and there are the ranges of alpha value which is multiplied with the source pixel to generate target pixel.). Alpha values also range from 0 to 255, with 0 being completely transparent (i.e., 0% opaque) and 255 completely opaque (i.e., 100% opaque. Alpha blending is a way of mixing the colors of two images together to form a final image. In the preset paper the authors tried to give a comprehensive review on different issues and scope in Alpha Compositing technology. A good example of naturally occurring alpha blending is a rainbow over a waterfall. The rainbow as one image, and the background waterfall is another, then the final image can be formed by alpha blending the two together. Keywords— Alpha Compositing; pixel; RGB; Android; Blending Equation I. -
AN5072: Introduction to Embedded Graphics – Application Note
Freescale Semiconductor Document Number: AN5072 Application Note Rev 0, 02/2015 Introduction to Embedded Graphics with Freescale Devices by: Luis Olea and Ioseph Martinez Contents 1 Introduction 1 Introduction................................................................1 The purpose of this application note is to explain the basic 1.1 Graphics basic concepts.............. ...................1 concepts and requirements of embedded graphics applications, 2 Freescale graphics devices.............. ..........................7 helping software and hardware engineers easily understand the fundamental concepts behind color display technology, which 2.1 MPC5606S.....................................................7 is being expected by consumers more and more in varied 2.2 MPC5645S.....................................................8 applications from automotive to industrial markets. 2.3 Vybrid.............................................................8 This section helps the reader to familiarize with some of the most used concepts when working with graphics. Knowing 2.4 MAC57Dxx....................... ............................ 8 these definitions is basic for any developer that wants to get 2.5 Kinetis K70......................... ...........................8 started with embedded graphics systems, and these concepts apply to any system that handles graphics irrespective of the 2.6 QorIQ LS1021A................... ......................... 9 hardware used. 2.7 i.MX family........................ ........................... 9 2.8 Quick -
Freeimage Documentation Here
FreeImage a free, open source graphics library Documentation Library version 3.13.1 Contents Introduction 1 Foreword ............................................................................................................................... 1 Purpose of FreeImage ........................................................................................................... 1 Library reference .................................................................................................................. 2 Bitmap function reference 3 General functions .................................................................................................................. 3 FreeImage_Initialise ............................................................................................... 3 FreeImage_DeInitialise .......................................................................................... 3 FreeImage_GetVersion .......................................................................................... 4 FreeImage_GetCopyrightMessage ......................................................................... 4 FreeImage_SetOutputMessage ............................................................................... 4 Bitmap management functions ............................................................................................. 5 FreeImage_Allocate ............................................................................................... 6 FreeImage_AllocateT ............................................................................................ -
3D Graphics Video
NVIDIA GEFORCE GTX 200 GPU DATASHEET 3D Graphics nd · 2 generation unified architecture 1 · Up to 240 processor cores · Next generation geometry shading and stream out performance · Next generation dual issue · Next generation HW scheduler · NVIDIA GigaThread™ technology with increased number of threads · 2x registers · Full support for Microsoft DirectX 10.0 Shader Model 4.0 and OpenGL 2.1 APIs · Full 128-bit floating point precision through the entire rendering pipeline · Lumenex™ Engine · 16× full screen antialiasing · Transparent multisampling and transparent supersampling · 16× angle independent anisotropic filtering · 128-bit floating point high dynamic-range (HDR) lighting with antialiasing · 32-bit per component floating point texture filtering and blending · Full speed frame buffer blending · Advanced lossless compression algorithms for color, texture, and z-data · Support for normal map compression · Z-cull · Early-Z Video 2 · PureVideo HD® Technology · Dedicated on-chip video processor · High-definition H.264, VC-1, MPEG2, and WMV9 decode acceleration · Blu-ray dual-stream hardware acceleration (supporting HD picture-in-picture playback) 3 · HDCP capable up to 2560×1600 resolution · Advanced spatial-temporal de-interlacing · Noise Reduction · Edge Enhancement · Bad Edit Correction · Inverse telecine (2:2 and 3:2 pull-down correction) · High-quality scaling · Video color correction · Microsoft Video Mixing Renderer (VMR) support · Dynamic Contrast and Tone Enhancements NVIDIA GEFORCE GTX 200 GPU DATASHEET NVIDIA Technology 4 -
Hardware Acceleration in the Context of Motion Control for Autonomous Vehicles
DEGREE PROJECT IN ELECTRICAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020 Hardware Acceleration in the Context of Motion Control for Autonomous Vehicles JELIN LESLIN KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Hardware Acceleration in the Context of Motion Control for Autonomous Systems JELIN LESLIN Master’s Programme, Embedded Systems, 120 credits Date: November 16, 2020 Supervisor: Masoumeh (Azin) Ebrahimi Examiner: Johnny Öberg Industry Supervisor: Markhusson Christoffer & Harikrishnan Girishkasthuri School of Electrical Engineering and Computer Science Host company: Volvo Trucks Swedish title: Hårdvaruacceleration i samband med rörelsekontroll för autonoma system Hardware Acceleration in the Context of Motion Control for Autonomous Systems / Hårdvaruacceleration i samband med rörelsekontroll för autonoma system © 2020 Jelin Leslin | i Abstract State estimation filters are computationally intensive blocks used to calculate uncertain/unknown state values from noisy/not available sensor inputs in any autonomous systems. The inputs to the actuators depend on these filter’s output and thus the scheduling of filter has to be at very small time intervals. The aim of this thesis is to investigate the possibility of using hardware accelerators to perform this computation. To make a comparative study, 3 filters that predicts 4, 8 and 16 state information was developed and implemented in Arm real time and application purpose CPU, NVIDIA Quadro and Turing GPU, and Xilinx FPGA programmable logic. The execution, memory transfer time, and the total developement time to realise the logic in CPU, GPU and FPGA is discussed. The CUDA developement environment was used for the GPU implementation and Vivado HLS with SDSoc environ- ment was used for the FPGA implementation. -
Hardware Acceleration of Electronic Design Automation
HARDWARE ACCELERATION OF ELECTRONIC DESIGN AUTOMATION ALGORITHMS A Dissertation by KANUPRIYA GULATI Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY December 2009 Major Subject: Computer Engineering HARDWARE ACCELERATION OF ELECTRONIC DESIGN AUTOMATION ALGORITHMS A Dissertation by KANUPRIYA GULATI Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Approved by: Chair of Committee, Sunil P. Khatri Committee Members, Peng Li Jim Ji Duncan M. Walker Desmond A. Kirkpatrick Head of Department, Costas N. Georghiades December 2009 Major Subject: Computer Engineering iii ABSTRACT Hardware Acceleration of Electronic Design Automation Algorithms. (December 2009) Kanupriya Gulati, B.E., Delhi College of Engineering, New Delhi, India; M.S., Texas A&M University Chair of Advisory Committee: Dr. Sunil P. Khatri With the advances in very large scale integration (VLSI) technology, hardware is going parallel. Software, which was traditionally designed to execute on single core microproces- sors, now faces the tough challenge of taking advantage of this parallelism, made available by the scaling of hardware. The work presented in this dissertation studies the accelera- tion of electronic design automation (EDA) software on several hardware platforms such as custom integrated circuits (ICs), field programmable gate arrays (FPGAs) and graphics processors. This dissertation concentrates on a subset of EDA algorithms which are heav- ily used in the VLSI design flow, and also have varying degrees of inherent parallelism in them. In particular, Boolean satisfiability, Monte Carlo based statistical static timing analysis, circuit simulation, fault simulation and fault table generation are explored. -
Alpha Compositing Alpha Compositing the Alpha Channel
Alpha Compositing • alpha compositing is the process of combining an image with a background to create the appearance of partial transparency. • In order to correctly combine these image elements, it is Alpha Compositing necessary to keep an associated matte for each element. • This matte contains the coverage information - the shape of the geometry being drawn • This allows us to distinguish between parts of the image where the geometry was actually drawn and other parts of the image which are empty. The Alpha Channel The Alpha Channel “A separate component is needed to retain the matte information, the extent of coverage of an element at a pixel. • To store this matte information, the concept of an alpha In a full colour rendering of an element, the RGB components retain only the channel was introduced by A. R. Smith (1970s) colour. In order to place the element over an arbitrary background, a mixing factor is required at every pixel to control the linear interpolation of foreground and • Porter and Duff then expanded this to give us the basic background colours. algebra of Compositing in the paper "Compositing Digital In general, there is no way to encode this component as part of the colour Images" in 1984. information. For anti-aliasing purposes, this mixing factor needs to be of comparable resolution to the colour channels. Let us call this an alpha channel, and let us treat an alpha of 0 to indicate no coverage, 1 to mean full coverage, with fractions corresponding to partial coverage.” Porter & Duff 84 Alpha Channel Pre multiplied alpha • In a 2D image element which stores a colour for each pixel, an additional value is “What is the meaning of the quadruple (r,g,b,a) at a pixel? stored in the alpha channel containing a value ranging from 0 to 1.