Recent International Trade Commission Representations
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SIMD Extensions
SIMD Extensions PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Sat, 12 May 2012 17:14:46 UTC Contents Articles SIMD 1 MMX (instruction set) 6 3DNow! 8 Streaming SIMD Extensions 12 SSE2 16 SSE3 18 SSSE3 20 SSE4 22 SSE5 26 Advanced Vector Extensions 28 CVT16 instruction set 31 XOP instruction set 31 References Article Sources and Contributors 33 Image Sources, Licenses and Contributors 34 Article Licenses License 35 SIMD 1 SIMD Single instruction Multiple instruction Single data SISD MISD Multiple data SIMD MIMD Single instruction, multiple data (SIMD), is a class of parallel computers in Flynn's taxonomy. It describes computers with multiple processing elements that perform the same operation on multiple data simultaneously. Thus, such machines exploit data level parallelism. History The first use of SIMD instructions was in vector supercomputers of the early 1970s such as the CDC Star-100 and the Texas Instruments ASC, which could operate on a vector of data with a single instruction. Vector processing was especially popularized by Cray in the 1970s and 1980s. Vector-processing architectures are now considered separate from SIMD machines, based on the fact that vector machines processed the vectors one word at a time through pipelined processors (though still based on a single instruction), whereas modern SIMD machines process all elements of the vector simultaneously.[1] The first era of modern SIMD machines was characterized by massively parallel processing-style supercomputers such as the Thinking Machines CM-1 and CM-2. These machines had many limited-functionality processors that would work in parallel. -
The Opengl ES Shading Language
The OpenGL ES® Shading Language Language Version: 3.20 Document Revision: 12 246 JuneAugust 2015 Editor: Robert J. Simpson, Qualcomm OpenGL GLSL editor: John Kessenich, LunarG GLSL version 1.1 Authors: John Kessenich, Dave Baldwin, Randi Rost 1 Copyright (c) 2013-2015 The Khronos Group Inc. All Rights Reserved. This specification is protected by copyright laws and contains material proprietary to the Khronos Group, Inc. It or any components may not be reproduced, republished, distributed, transmitted, displayed, broadcast, or otherwise exploited in any manner without the express prior written permission of Khronos Group. You may use this specification for implementing the functionality therein, without altering or removing any trademark, copyright or other notice from the specification, but the receipt or possession of this specification does not convey any rights to reproduce, disclose, or distribute its contents, or to manufacture, use, or sell anything that it may describe, in whole or in part. Khronos Group grants express permission to any current Promoter, Contributor or Adopter member of Khronos to copy and redistribute UNMODIFIED versions of this specification in any fashion, provided that NO CHARGE is made for the specification and the latest available update of the specification for any version of the API is used whenever possible. Such distributed specification may be reformatted AS LONG AS the contents of the specification are not changed in any way. The specification may be incorporated into a product that is sold as long as such product includes significant independent work developed by the seller. A link to the current version of this specification on the Khronos Group website should be included whenever possible with specification distributions. -
The Opengl ES Shading Language
The OpenGL ES® Shading Language Language Version: 3.00 Document Revision: 6 29 January 2016 Editor: Robert J. Simpson, Qualcomm OpenGL GLSL editor: John Kessenich, LunarG GLSL version 1.1 Authors: John Kessenich, Dave Baldwin, Randi Rost Copyright © 2008-2016 The Khronos Group Inc. All Rights Reserved. This specification is protected by copyright laws and contains material proprietary to the Khronos Group, Inc. It or any components may not be reproduced, republished, distributed, transmitted, displayed, broadcast, or otherwise exploited in any manner without the express prior written permission of Khronos Group. You may use this specification for implementing the functionality therein, without altering or removing any trademark, copyright or other notice from the specification, but the receipt or possession of this specification does not convey any rights to reproduce, disclose, or distribute its contents, or to manufacture, use, or sell anything that it may describe, in whole or in part. Khronos Group grants express permission to any current Promoter, Contributor or Adopter member of Khronos to copy and redistribute UNMODIFIED versions of this specification in any fashion, provided that NO CHARGE is made for the specification and the latest available update of the specification for any version of the API is used whenever possible. Such distributed specification may be reformatted AS LONG AS the contents of the specification are not changed in any way. The specification may be incorporated into a product that is sold as long as such product includes significant independent work developed by the seller. A link to the current version of this specification on the Khronos Group website should be included whenever possible with specification distributions. -
Semiconductor Industry Merger and Acquisition Activity from an Intellectual Property and Technology Maturity Perspective
Semiconductor Industry Merger and Acquisition Activity from an Intellectual Property and Technology Maturity Perspective by James T. Pennington B.S. Mechanical Engineering (2011) University of Pittsburgh Submitted to the System Design and Management Program in Partial Fulfillment of the Requirements for the Degree of Master of Science in Engineering and Management at the Massachusetts Institute of Technology September 2020 © 2020 James T. Pennington All rights reserved The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created. Signature of Author ____________________________________________________________________ System Design and Management Program August 7, 2020 Certified by __________________________________________________________________________ Bruce G. Cameron Thesis Supervisor System Architecture Group Director in System Design and Management Accepted by __________________________________________________________________________ Joan Rubin Executive Director, System Design & Management Program THIS PAGE INTENTIALLY LEFT BLANK 2 Semiconductor Industry Merger and Acquisition Activity from an Intellectual Property and Technology Maturity Perspective by James T. Pennington Submitted to the System Design and Management Program on August 7, 2020 in Partial Fulfillment of the Requirements for the Degree of Master of Science in System Design and Management ABSTRACT A major method of acquiring the rights to technology is through the procurement of intellectual property (IP), which allow companies to both extend their technological advantage while denying it to others. Public databases such as the United States Patent and Trademark Office (USPTO) track this exchange of technology rights. Thus, IP can be used as a public measure of value accumulation in the form of technology rights. -
Application Specific Programmable Processors for Reconfigurable Self-Powered Devices
C651etukansi.kesken.fm Page 1 Monday, March 26, 2018 2:24 PM C 651 OULU 2018 C 651 UNIVERSITY OF OULU P.O. Box 8000 FI-90014 UNIVERSITY OF OULU FINLAND ACTA UNIVERSITATISUNIVERSITATIS OULUENSISOULUENSIS ACTA UNIVERSITATIS OULUENSIS ACTAACTA TECHNICATECHNICACC Teemu Nyländen Teemu Nyländen Teemu University Lecturer Tuomo Glumoff APPLICATION SPECIFIC University Lecturer Santeri Palviainen PROGRAMMABLE Postdoctoral research fellow Sanna Taskila PROCESSORS FOR RECONFIGURABLE Professor Olli Vuolteenaho SELF-POWERED DEVICES University Lecturer Veli-Matti Ulvinen Planning Director Pertti Tikkanen Professor Jari Juga University Lecturer Anu Soikkeli Professor Olli Vuolteenaho UNIVERSITY OF OULU GRADUATE SCHOOL; UNIVERSITY OF OULU, FACULTY OF INFORMATION TECHNOLOGY AND ELECTRICAL ENGINEERING Publications Editor Kirsti Nurkkala ISBN 978-952-62-1874-8 (Paperback) ISBN 978-952-62-1875-5 (PDF) ISSN 0355-3213 (Print) ISSN 1796-2226 (Online) ACTA UNIVERSITATIS OULUENSIS C Technica 651 TEEMU NYLÄNDEN APPLICATION SPECIFIC PROGRAMMABLE PROCESSORS FOR RECONFIGURABLE SELF-POWERED DEVICES Academic dissertation to be presented, with the assent of the Doctoral Training Committee of Technology and Natural Sciences of the University of Oulu, for public defence in the Wetteri auditorium (IT115), Linnanmaa, on 7 May 2018, at 12 noon UNIVERSITY OF OULU, OULU 2018 Copyright © 2018 Acta Univ. Oul. C 651, 2018 Supervised by Professor Olli Silvén Reviewed by Professor Leonel Sousa Doctor John McAllister ISBN 978-952-62-1874-8 (Paperback) ISBN 978-952-62-1875-5 (PDF) ISSN 0355-3213 (Printed) ISSN 1796-2226 (Online) Cover Design Raimo Ahonen JUVENES PRINT TAMPERE 2018 Nyländen, Teemu, Application specific programmable processors for reconfigurable self-powered devices. University of Oulu Graduate School; University of Oulu, Faculty of Information Technology and Electrical Engineering Acta Univ. -
2018 Nvidia Corporation Annual Review
2018 NVIDIA CORPORATION ANNUAL REVIEW NOTICE OF ANNUAL MEETING PROXY STATEMENT FORM 10-K GPUS ARE POWERING SOME OF THE HOTTEST TRENDS NOW AND FOR DECADES TO COME. YAHOO FINANCE Scientists using GPU computing were able to “see” gravitational waves for the first time in human history. Twenty-five years ago, we set out to transform computer graphics. Fueled by the massive growth of the gaming market and its insatiable demand for better 3D graphics, we’ve evolved the GPU into a computer brain at the intersection of virtual reality, high performance computing, and artificial intelligence. NVIDIA GPU computing has become the essential tool of the da Vincis and Einsteins of our time. For them, we’ve built the equivalent of a time machine. Jensen Huang CEO and Founder, NVIDIA Hellblade: Senua’s Sacrifice, as captured by NVIDIA Ansel, a powerful in-game camera that lets users take professional- grade photographs of their games. NVIDIA GEFORCE HAS MOVED FROM GRAPHICS CARD TO GAMING PLATFORM. FORBES At $100 billion, computer gaming is the world’s largest entertainment industry. And with 200 million gamers, NVIDIA GeForce is its largest platform. We continuously innovate across the GeForce platform, from GeForce GTX GPUs to GeForce Experience, and in new technologies like Max-Q that make gaming laptops thinner, quieter and faster. And we’re opening high-end gaming and blockbuster titles to millions of users for the first time with NVIDIA GeForce NOW, a cloud-based service that turns everyday PC and Mac laptops into virtual GeForce gaming machines. NVIDIA’S LATEST TECH IS HERE TO BLOW POSSIBILITIES OF VR WIDE OPEN. -
The GPU Computing Revolution
The GPU Computing Revolution From Multi-Core CPUs to Many-Core Graphics Processors A Knowledge Transfer Report from the London Mathematical Society and Knowledge Transfer Network for Industrial Mathematics By Simon McIntosh-Smith Copyright © 2011 by Simon McIntosh-Smith Front cover image credits: Top left: Umberto Shtanzman / Shutterstock.com Top right: godrick / Shutterstock.com Bottom left: Double Negative Visual Effects Bottom right: University of Bristol Background: Serg64 / Shutterstock.com THE GPU COMPUTING REVOLUTION From Multi-Core CPUs To Many-Core Graphics Processors By Simon McIntosh-Smith Contents Page Executive Summary 3 From Multi-Core to Many-Core: Background and Development 4 Success Stories 7 GPUs in Depth 11 Current Challenges 18 Next Steps 19 Appendix 1: Active Researchers and Practitioner Groups 21 Appendix 2: Software Applications Available on GPUs 23 References 24 September 2011 A Knowledge Transfer Report from the London Mathematical Society and the Knowledge Transfer Network for Industrial Mathematics Edited by Robert Leese and Tom Melham London Mathematical Society, De Morgan House, 57–58 Russell Square, London WC1B 4HS KTN for Industrial Mathematics, Surrey Technology Centre, Surrey Research Park, Guildford GU2 7YG 2 THE GPU COMPUTING REVOLUTION From Multi-Core CPUs To Many-Core Graphics Processors AUTHOR Simon McIntosh-Smith is head of the Microelectronics Research Group at the Univer- sity of Bristol and chair of the Many-Core and Reconfigurable Supercomputing Conference (MRSC), Europe’s largest conference dedicated to the use of massively parallel computer architectures. Prior to joining the university he spent fifteen years in industry where he designed massively parallel hardware and software at companies such as Inmos, STMicro- electronics and Pixelfusion, before co-founding ClearSpeed as Vice-President of Architec- ture and Applications. -
GPU Developments 2017T
GPU Developments 2017 2018 GPU Developments 2017t © Copyright Jon Peddie Research 2018. All rights reserved. Reproduction in whole or in part is prohibited without written permission from Jon Peddie Research. This report is the property of Jon Peddie Research (JPR) and made available to a restricted number of clients only upon these terms and conditions. Agreement not to copy or disclose. This report and all future reports or other materials provided by JPR pursuant to this subscription (collectively, “Reports”) are protected by: (i) federal copyright, pursuant to the Copyright Act of 1976; and (ii) the nondisclosure provisions set forth immediately following. License, exclusive use, and agreement not to disclose. Reports are the trade secret property exclusively of JPR and are made available to a restricted number of clients, for their exclusive use and only upon the following terms and conditions. JPR grants site-wide license to read and utilize the information in the Reports, exclusively to the initial subscriber to the Reports, its subsidiaries, divisions, and employees (collectively, “Subscriber”). The Reports shall, at all times, be treated by Subscriber as proprietary and confidential documents, for internal use only. Subscriber agrees that it will not reproduce for or share any of the material in the Reports (“Material”) with any entity or individual other than Subscriber (“Shared Third Party”) (collectively, “Share” or “Sharing”), without the advance written permission of JPR. Subscriber shall be liable for any breach of this agreement and shall be subject to cancellation of its subscription to Reports. Without limiting this liability, Subscriber shall be liable for any damages suffered by JPR as a result of any Sharing of any Material, without advance written permission of JPR. -
Acceleration of Deep Learning on FPGA
University of Windsor Scholarship at UWindsor Electronic Theses and Dissertations Theses, Dissertations, and Major Papers 4-14-2017 Acceleration of Deep Learning on FPGA Huyuan Li University of Windsor Follow this and additional works at: https://scholar.uwindsor.ca/etd Recommended Citation Li, Huyuan, "Acceleration of Deep Learning on FPGA" (2017). Electronic Theses and Dissertations. 5947. https://scholar.uwindsor.ca/etd/5947 This online database contains the full-text of PhD dissertations and Masters’ theses of University of Windsor students from 1954 forward. These documents are made available for personal study and research purposes only, in accordance with the Canadian Copyright Act and the Creative Commons license—CC BY-NC-ND (Attribution, Non-Commercial, No Derivative Works). Under this license, works must always be attributed to the copyright holder (original author), cannot be used for any commercial purposes, and may not be altered. Any other use would require the permission of the copyright holder. Students may inquire about withdrawing their dissertation and/or thesis from this database. For additional inquiries, please contact the repository administrator via email ([email protected]) or by telephone at 519-253-3000ext. 3208. Acceleration of Deep Learning on FPGA by Huyuan Li A Thesis Submitted to the Faculty of Graduate Studies through the Department of Electrical and Computer Engineering in Partial Fulfillment of the Requirements for the Degree of Master of Applied Science at the University of Windsor Windsor, Ontario, Canada 2017 © 2017, Huyuan Li Acceleration of Deep Learning on FPGA by Huyuan Li APPROVED BY: T. Bolisetti Department of Civil and Environmental Engineering H. -
Università Degli Studi Di Parma Service Oriented
UNIVERSITÀ DEGLI STUDI DI PARMA DIPARTIMENTO DI INGEGNERIA DELL’INFORMAZIONE Dottorato di Ricerca in Tecnologie dell’Informazione XXII Ciclo Maria Chiara Laghi SERVICE ORIENTED MOBILE COMPUTING DISSERTAZIONE PRESENTATA PER IL CONSEGUIMENTO DEL TITOLO DI DOTTORE DI RICERCA GENNAIO 2010 UNIVERSITÀ DEGLI STUDI DI PARMA Dottorato di Ricerca in Tecnologie dell’Informazione XXII Ciclo SERVICE ORIENTED MOBILE COMPUTING Coordinatore: Chiar.mo Prof. Carlo Morandi Tutor: Chiar.mo Prof. Gianni Conte Dottorando: Maria Chiara Laghi Gennaio 2010 To my family and my friends Contents Introduction1 1 State of the Art5 1.1 Pervasive and ubiquitous computing.................8 1.1.1 Pervasive computing emerging paradigms.......... 11 1.2 Mobile Computing.......................... 13 1.2.1 Mobile hardware....................... 24 1.2.2 Mobile software platforms and applications......... 37 1.2.3 Java Micro Edition (J2ME).................. 47 1.2.4 Comparison between most diffused devices and platforms. 49 1.3 Service oriented infrastructures for pervasive computing...... 55 1.3.1 Ubiquitous peer-to-peer sharing of services......... 56 1.3.2 Web Services on resource-constrained devices....... 58 2 Framework 61 2.1 Networked Autonomic Machine................... 61 2.1.1 Services as NAM resources................. 63 2.1.2 Service composition..................... 65 2.1.3 Related works......................... 67 2.1.4 NSAM for p2p service-oriented infrastructure........ 71 2.2 Code Mobility............................. 74 2.2.1 Resource and Service migration............... 76 ii Contents 3 Middleware and Applications 77 3.1 Ubiquitous p2p sharing of services: JXTA-SOAP mobile...... 78 3.1.1 Service deployment...................... 80 3.1.2 Service publication and lookup................ 82 3.1.3 Service invocation..................... -
001PR-Creative Launches Zii Platform
CONTACT INFORMATION Phil O’Shaughnessy Tim Lewis Wynne Leong Creative Labs, Inc. ZiiLABS Pte Ltd Creative Technology Ltd. +1 408 546 6773 +44 (0)1784 476 651 +65 6895 4120 [email protected] [email protected] [email protected] Creative Launches the Zii ™ Platform and Ushers in the Era of StemCell Computing ™ Creative Forms ZiiLABS and Ushers in the Era of StemCell Computing ™ Where Nano-Sized Super Computing Will Be Available in Our Daily Lives through Flexible, Tiny, Powerful and Scalable SoC SINGAPORE – 8 January 2009 – Creative Technology Ltd. today announced the formation of ZiiLABS (a wholly-owned subsidiary of Creative), with the combination of 3DLABS — which has a 25-year history as a leading innovator in programmable graphics, media and applications processing — and resources drawn from the largest product group in Creative, the Personal Digital Entertainment group. The formation of ZiiLABS, the launch of the new ZiiLABS ZMS-05 SoC (System-On-Chip) and the new Zii™ Platform today usher in the new era of StemCell Computing™. The new ZiiLABS ZMS-05 SoC (System-On-Chip) will be unveiled and demonstrated from 8-11 January 2009 at the Consumer Electronics Show in Las Vegas in the Creative exhibit #30651 in the South Hall of the Las Vegas Convention Center. “We have invested a billion dollars and 10,000 man years of R&D effort over the last 25 years in platform solutions,” said Sim Wong Hoo, Chairman and CEO of Creative Technology Ltd. “The combination of the technology from 3DLABS and the product development prowess of our Personal Digital Entertainment group delivers a complete, powerful and energy efficient platform with a very rapid time-to-market for our partners – the Zii Platform.” “We now look to shaping the future of computing with the introduction of the integrated ZMS- 05 media-rich processor, and ushering in the new era of StemCell Computing where we will bring the incredible benefits of nano-sized super computing right into our daily lives,” said Hock Leow, President of ZiiLABS. -
Company Vendor ID (Decimal Format) (AVL) Ditest Fahrzeugdiagnose Gmbh 4621 @Pos.Com 3765 0XF8 Limited 10737 1MORE INC
Vendor ID Company (Decimal Format) (AVL) DiTEST Fahrzeugdiagnose GmbH 4621 @pos.com 3765 0XF8 Limited 10737 1MORE INC. 12048 360fly, Inc. 11161 3C TEK CORP. 9397 3D Imaging & Simulations Corp. (3DISC) 11190 3D Systems Corporation 10632 3DRUDDER 11770 3eYamaichi Electronics Co., Ltd. 8709 3M Cogent, Inc. 7717 3M Scott 8463 3T B.V. 11721 4iiii Innovations Inc. 10009 4Links Limited 10728 4MOD Technology 10244 64seconds, Inc. 12215 77 Elektronika Kft. 11175 89 North, Inc. 12070 Shenzhen 8Bitdo Tech Co., Ltd. 11720 90meter Solutions, Inc. 12086 A‐FOUR TECH CO., LTD. 2522 A‐One Co., Ltd. 10116 A‐Tec Subsystem, Inc. 2164 A‐VEKT K.K. 11459 A. Eberle GmbH & Co. KG 6910 a.tron3d GmbH 9965 A&T Corporation 11849 Aaronia AG 12146 abatec group AG 10371 ABB India Limited 11250 ABILITY ENTERPRISE CO., LTD. 5145 Abionic SA 12412 AbleNet Inc. 8262 Ableton AG 10626 ABOV Semiconductor Co., Ltd. 6697 Absolute USA 10972 AcBel Polytech Inc. 12335 Access Network Technology Limited 10568 ACCUCOMM, INC. 10219 Accumetrics Associates, Inc. 10392 Accusys, Inc. 5055 Ace Karaoke Corp. 8799 ACELLA 8758 Acer, Inc. 1282 Aces Electronics Co., Ltd. 7347 Aclima Inc. 10273 ACON, Advanced‐Connectek, Inc. 1314 Acoustic Arc Technology Holding Limited 12353 ACR Braendli & Voegeli AG 11152 Acromag Inc. 9855 Acroname Inc. 9471 Action Industries (M) SDN BHD 11715 Action Star Technology Co., Ltd. 2101 Actions Microelectronics Co., Ltd. 7649 Actions Semiconductor Co., Ltd. 4310 Active Mind Technology 10505 Qorvo, Inc 11744 Activision 5168 Acute Technology Inc. 10876 Adam Tech 5437 Adapt‐IP Company 10990 Adaptertek Technology Co., Ltd. 11329 ADATA Technology Co., Ltd.