Mobile Application and Multimedia Processors
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RISC-V Core Out-Clocks Apple, Sifive; Available As IP
RISC-V core out-clocks Apple, SiFive; available as IP Movember 05, 2020 //By Peter Clarke Micro Magic Inc. (Sunnyvale, Calif.) has functioning silicon of its 5GHz-capable 64bit RISC-V processor and is offering the design as intellectual property. The Micro Magic processor out-clocks the Apple A14 bionic, the processor at the heart of the iPhone 12 and one of the first processors on 5nm silicon. It also goes faster than a quad-core U84 CPU that SiFive states can operate at up to 2.6GHz clock frequency when implemented in a 7nm process. Micro Magic has a history that goes back to Sun Microsystems and beyond (see EDA company claims world’s fastest 64bit RISC-V core). It is reportedly one of Silicon Valley’s well-kept secrets and a go-to resource for design teams trying to remove bottlenecks in their datapath designs. Andy Huang, an independent contractor who supports Micro Magic for marketing and business development functions, contacted eeNews Europe and demonstrated the processor running EEMBC CoreMark benchmarks over a Facetime connection. Huang was founder and CEO of ACAD Corp., the developer of the Finesim simulator, one of the first and fastest of parallel SPICE simulators. ACAD was acquired by Magma Design Automation in 2006 before Magma itself was acquired by Synopsys in 2012. Huang declined to say which foundry had manufactured silicon for Micro Magic or in what manufacturing process it had been implemented. Huang said the that processor is made in a FinFET process and was manufactured using a multiproject wafer (MPW) run. -
5G: Perspectives from a Chipmaker 5G Electronic Workshop, LETI Innovation Days – June 2019
5G: Perspectives from a Chipmaker 5G electronic workshop, LETI Innovation Days – June 2019 Guillaume Vivier Sequans communications 1 ©2019 Sequans Communications |5G: Perspective from a chip maker – June 2019 MKT-FM-002-R15 Outline • Context, background, market • 5G chipmaker: process technology thoughts and challenges • Conclusion 2 ©2019 Sequans Communications |5G: Perspective from a chip maker – June 2019 5G overall landscape • 3GPP standardization started in Sep 2015 – 5G is wider than RAN (includes new core) – Rel. 15 completed in Dec 2018. ASN1 freeze for 4G-5G migration options in June 19 – Rel. 16 on-going, to be completed in Dec 2019 (June 2020) • Trials and more into 201 operators, 80+ countries (source GSA) • Commercial deployments announced in – Korea, USA, China, Australia, UAE 3 ©2019 Sequans Communications |5G: Perspective from a chip maker – June 2019 Ericsson Mobility Report Nov 2018 • “In 2024, we project that 5G will reach 40 percent population coverage and 1.5 billion subscriptions“ • Interestingly, the report highlights the fact that IoT will continue to grow, beyond LWPA, leveraging higher capability of LTE and 5G 4 ©2019 Sequans Communications |5G: Perspective from a chip maker – June 2019 5G overall landscape • eMBB: smartphone and FWA market – Main focus so far from the ecosystem • URLLC: the next wave – Verticals: Industry 4.0, gaming, media Private LTE/5G deployment, … – V2X and connected car • mMTC: – LPWA type of communication is served by cat-M and NB-IoT – 5G opens the door to new IoT cases not served by LPWA, • Example surveillance camera with image processing on the device • Flexibility is key – From Network side, NVF, SDN, Slicing, etc. -
An Optimized H.266/VVC Software Decoder on Mobile Platform
An Optimized H.266/VVC Software Decoder On Mobile Platform Yiming Li, Shan Liu, Yu Chen, Yushan Zheng, Sijia Chen, Bin Zhu, Jian Lou Tencent Media Lab, Shenzhen, China and Palo Alto, CA, USA, fmarcli, [email protected] Abstract—As the successor of H.265/HEVC, the new versatile standard. Therefore, it is essential to have an efficient and video coding standard (H.266/VVC) can provide up to 50% optimized software decoder implementation to support the bitrate saving with the same subjective quality, at the cost of emerging applications. In [3] [4], an independent VVC soft- increased decoding complexity. To accelerate the application of the new coding standard, a real-time H.266/VVC software ware decoder implemented by Tencent demonstrated real-time decoder that can support various platforms is implemented, HD/UHD decoding capability on x86 platform. Considering where SIMD technologies, parallelism optimization, and the that mobile devices have become an essential carrier and acceleration strategies based on the characteristics of each coding display tool for video services, extensive optimization efforts tool are applied. As the mobile devices have become an essential were made on top of the framework of [3] to achieve real- carrier for video services nowadays, the mentioned optimization efforts are not only implemented for the x86 platform, but more time HD/UHD decoding on the mobile platform. As a result, importantly utilized to highly optimize the decoding performance a uniform-designed software H.266/VVC decoder that can on the ARM platform in this work. The experimental results show run real-time on different platforms and supports versatile that when running on the Apple A14 SoC (iPhone 12pro), the av- functionalities such as screen content coding (SCC) is ac- erage single-thread decoding speed of the present implementation complished. -
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big data and cognitive computing Article Fast and Effective Retrieval for Large Multimedia Collections Stefan Wagenpfeil 1,* , Binh Vu 1 , Paul Mc Kevitt 2 and Matthias Hemmje 1 1 Faculty of Mathematics and Computer Science, University of Hagen, Universitätsstrasse 1, D-58097 Hagen, Germany; [email protected] (B.V.); [email protected] (M.H.) 2 Academy for International Science & Research (AISR), Derry BT48 7JL, UK; [email protected] * Correspondence: [email protected] Abstract: The indexing and retrieval of multimedia content is generally implemented by employing feature graphs. These graphs typically contain a significant number of nodes and edges to reflect the level of detail in feature detection. A higher level of detail increases the effectiveness of the results, but also leads to more complex graph structures. However, graph traversal-based algorithms for similarity are quite inefficient and computationally expensive, especially for large data structures. To deliver fast and effective retrieval especially for large multimedia collections and multimedia big data, an efficient similarity algorithm for large graphs in particular is desirable. Hence, in this paper, we define a graph projection into a 2D space (Graph Code) and the corresponding algorithms for indexing and retrieval. We show that calculations in this space can be performed more efficiently than graph traversals due to the simpler processing model and the high level of parallelization. As a consequence, we demonstrate experimentally that the effectiveness of retrieval also increases substantially, as the Graph Code facilitates more levels of detail in feature fusion. These levels of detail also support an increased trust prediction, particularly for fused social media content. -
EDIT THIS 2021 ISRI 1201 Post-Hearing Letter 050621
Juelsgaard Intellectual Property and Innovation Clinic Mills Legal Clinic Stanford Law School Crown Quadrangle May 7, 2021 559 Nathan Abbott Way Stanford, CA 94305-8610 [email protected] Regan Smith 650.724.1900 Mark Gray United States Copyright Office [email protected] [email protected] Re: Docket No. 2020-11 Exemptions to Prohibition Against Circumvention of Technological Measures Protecting Copyrighted Works Dear Ms. Smith and Mr. Gray: I write to respond to your April 27 post-hearing letter requesting the materials that I referenced during the April 21 hearing related to Proposed Class 10 (Computer Programs – Unlocking) that were not included in our written comments. In particular, I cited to three reports from the Global mobile Suppliers Association (“GSA”) to illustrate the rapid increase in cellular-enabled devices with 5G capabilities in the last three years. In March 2019, GSA had identified 33 announced 5G devices from 23 vendors in 7 different form factors.1 By March 2020, GSA had identified 253 announced 5G devices from 81 vendors in 16 different form factors, including the first 5G-enabled laptops, TVs, and tablets.2 And by April 2021, GSA had identified 703 announced 5G devices from 122 vendors in 22 different form factors.3 It should be noted that some of the 22 form factors, such as 5G modules,4 can be deployed across a wide range of use cases that are not directly tracked by the GSA reports.5 For example, one distributor of Quectel’s 5G modules described the target applications as including: Telematics & transport – vehicle tracking, asset tracking, fleet management Energy – electricity meters, gas/water meter, smart grid Payment – wireless pos [point of service], cash register, ATM, vending machine Security – surveillance, detectors Smart city – street lighting, smart parking, sharing economy Gateway – consumer/industrial router 1 GSA, 5G Device Ecosystem (Mar. -
5G, Lte & Iot Components Vendors Profiled (28)
5G, LTE & IOT COMPONENTS VENDORS PROFILED (28) Altair Semiconductor Ltd., a subsidiary of Sony Corp. / www.altair-semi.com Analog Devices Inc. (NYSE: ADI) / www.analog.com ARM Ltd., a subsidiary of SoftBank Group Corp. / www.arm.com Blu Wireless Technology Ltd. / www.bluwirelesstechnology.com Broadcom Corp. (Nasdaq: BRCM) / www.broadcom.com Cadence Design Systems Inc. / www.cadence.com Ceva Inc. (Nasdaq: CEVA) / www.ceva-dsp.com eASIC Corp. / www.easic.com GCT Semiconductor Inc. / www.gctsemi.com HiSilicon Technologies Co. Ltd. / www.hisilicon.com Integrated Device Technology Inc. (Nasdaq: IDTI) / www.idt.com Intel Corp. (Nasdaq: INTC) / www.intel.com Lime Microsystems Ltd. / www.limemicro.com Marvell Technology Group Ltd. (Nasdaq: MRVL) / www.marvell.com MediaTek Inc. / www.mediatek.com Microsemi Corp., a subsidiary of Microchip Technology Inc. (Nasdaq: MCHP) / www.microsemi.com MIPS, an IP licensing business unit of Wave Computing Inc. / www.mips.com Nordic Semiconductor ASA (OSX: NOD) / www.nordicsemi.com NXP Semiconductors N.V. (Nasdaq: NXPI) / www.nxp.com Octasic Inc. / www.octasic.com Peraso Technologies Inc. / www.perasotech.com Qualcomm Inc. (Nasdaq: QCOM) / www.qualcomm.com Samsung Electronics Co. Ltd. (005930:KS) / www.samsung.com Sanechips Technology Co. Ltd., a subsidiary of ZTE Corp. (SHE: 000063) / www.sanechips.com.cn Sequans Communications S.A. (NYSE: SQNS) / www.sequans.com Texas Instruments Inc. (NYSE: TXN) / www.ti.com Unisoc Communications Inc., a subsidiary of Tsinghua Unigroup Ltd. / www.unisoc.com Xilinx Inc. (Nasdaq: XLNX) / www.xilinx.com © HEAVY READING | AUGUST 2018 | 5G/LTE BASE STATION, RRH, CPE & IOT COMPONENTS . -
Download Magazine
Magazine Issue Porsche Engineering 1/2021 www.porsche-engineering.com AUTOMOTIVE DEVELOPMENT ON THE MOVE Next level As always, our strongest motivation: our own standards. The new Panamera S E-Hybrid. Drive defines us. How can you measure progress? The new Panamera and its numbers make for a good start. Its .-litre twin-turbo V engine combines with the electric motor to produce a system output of kW (PS). And this new power is backed with an increased electric range. Only the best to drive your next project forward. Discover more at www.porsche.com/Panamera Fuel consumption (in l/km) combined . – .; CO₂ emissions (in g/km) combined –; electricity consumption (in kWh/km) combined .–. Porsche Engineering Magazine 1/2021 EDITORIAL 3 Dear Reader, “Next level”—scarcely any other expression sums the situation in which we currently find ourselves more succinctly: In many fields, we simply have to reach the next level. We are familiar with the term from the worlds of computer games and sports. Those who have reached a certain level there often move on to the next level right away. This also applies to our work in automotive development: We cannot rest on our laurels. We see this in trends such as artificial intelligence, the use of game engines in vehicle development and future E/E architectures, which we examine in the title series. They exemplify the increasing complexity of our work. We also have to reach the next level in our methods. In his article, Marius Mihailovici, Managing Director of our digital branch in Cluj, explains what this looks like in the software development sector. -
Thread-Level Parallelism I
Great Ideas in UC Berkeley UC Berkeley Teaching Professor Computer Architecture Professor Dan Garcia (a.k.a. Machine Structures) Bora Nikolić Thread-Level Parallelism I Garcia, Nikolić cs61c.org Improving Performance 1. Increase clock rate fs ú Reached practical maximum for today’s technology ú < 5GHz for general purpose computers 2. Lower CPI (cycles per instruction) ú SIMD, “instruction level parallelism” Today’s lecture 3. Perform multiple tasks simultaneously ú Multiple CPUs, each executing different program ú Tasks may be related E.g. each CPU performs part of a big matrix multiplication ú or unrelated E.g. distribute different web http requests over different computers E.g. run pptx (view lecture slides) and browser (youtube) simultaneously 4. Do all of the above: ú High fs , SIMD, multiple parallel tasks Garcia, Nikolić 3 Thread-Level Parallelism I (3) New-School Machine Structures Software Harness Hardware Parallelism & Parallel Requests Achieve High Assigned to computer Performance e.g., Search “Cats” Smart Phone Warehouse Scale Parallel Threads Computer Assigned to core e.g., Lookup, Ads Computer Core Core Parallel Instructions Memory (Cache) >1 instruction @ one time … e.g., 5 pipelined instructions Input/Output Parallel Data Exec. Unit(s) Functional Block(s) >1 data item @ one time A +B A +B e.g., Add of 4 pairs of words 0 0 1 1 Main Memory Hardware descriptions Logic Gates A B All gates work in parallel at same time Out = AB+CD C D Garcia, Nikolić Thread-Level Parallelism I (4) Parallel Computer Architectures Massive array -
956830 Deliverable D2.1 Initial Vision and Requirement Report
European Core Technologies for future connectivity systems and components Call/Topic: H2020 ICT-42-2020 Grant Agreement Number: 956830 Deliverable D2.1 Initial vision and requirement report Deliverable type: Report WP number and title: WP2 (Strategy, vision, and requirements) Dissemination level: Public Due date: 31.12.2020 Lead beneficiary: EAB Lead author(s): Fredrik Tillman (EAB), Björn Ekelund (EAB) Contributing partners: Yaning Zou (TUD), Uta Schneider (TUD), Alexandros Kaloxylos (5G IA), Patrick Cogez (AENEAS), Mohand Achouche (IIIV/Nokia), Werner Mohr (IIIV/Nokia), Frank Hofmann (BOSCH), Didier Belot (CEA), Jochen Koszescha (IFAG), Jacques Magen (AUS), Piet Wambacq (IMEC), Björn Debaillie (IMEC), Patrick Pype (NXP), Frederic Gianesello (ST), Raphael Bingert (ST) Reviewers: Mohand Achouche (IIIV/Nokia), Jacques Magen (AUS), Yaning Zou (TUD), Alexandros Kaloxylos (5G IA), Frank Hofmann (BOSCH), Piet Wambacq (IMEC), Patrick Cogez (AENEAS) D 2.1 – Initial vision and requirement report Document History Version Date Author/Editor Description 0.1 05.11.2020 Fredrik Tillman (EAB) Outline and contributors 0.2 19.11.2020 All contributors First complete draft 0.3 18.12.2020 All contributors Second complete draft 0.4 21.12.2020 Björn Ekelund Third complete draft 1.0 21.12.2020 Fredrik Tillman (EAB) Final version List of Abbreviations Abbreviation Denotation 5G 5th Generation of wireless communication 5G PPP The 5G infrastructure Public Private Partnership 6G 6th Generation of wireless communication AI Artificial Intelligence ASIC Application -
Spreadtrum Android IMEI Toolrar
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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. -
Fomalhaut Techno Solutions★ Minatake Mitchell Kashio Fomalhaut 4G/5G WIRELESS NETWORK SYSTEM Techno Solutions★ ②
Base Station & Smartphone Source: Huawei Fomalhaut Techno Solutions★ Minatake Mitchell Kashio Fomalhaut 4G/5G WIRELESS NETWORK SYSTEM Techno Solutions★ ② ② ① ③ Source: maximintegrated.com (C) Fomalhaut Techno Solutions. All rights reserved. / Phone: +81-3-6759-4289 / Mail: [email protected] 5G Base Station & Smartphone 2 Fomalhaut 1. HUAWEI 5G BASEBAND UNIT BBU5900 Techno Solutions★ 4G BBU 5G SUB-6 BBU MASTER BBU (C) Fomalhaut Techno Solutions. All rights reserved. / Phone: +81-3-6759-4289 / Mail: [email protected] 5G Base Station & Smartphone 3 Fomalhaut Techno Solutions MASTER BBU 1 notch = 1mm ★ OSFP Port (mnf. unknown) (assumption) CDFP Port (mnf. unknown) (assumption) DRAM (Samsung) K4A8G165WB Flash Memory (Macronix) MX66U51235F Application Processor (HiSilicon) Hi1383 (assumption) Network Processor (HiSilicon) SD6603 (assumption) Unknown (TI) LMK05805B2 Network Processor (HiSilicon) SD6186 (assumption) DRAM (Samsung) K4A4G165WE FPGA (Xilinx) KINTEX XCKU5P (C) Fomalhaut Techno Solutions. All rights reserved. / Phone: +81-3-6759-4289 / Mail: [email protected] 5G Base Station & Smartphone 4 Fomalhaut Techno Solutions 5G SUB-6 BBU 1 notch = 1mm ★ LAN Filter (MNC) G24117CE 1735X (assumption) LAN Filter (Delta) 5G LFE8505 Gigabit Ethernet Transceiver (Marvell) 88E1322 10/100/1000 Base-T Gigabit Ethernet Transceiver (Broadcom) BCM54219 DRAM (Samsung) OSFP Port K4A8G165WB (mnf. unknown) (assumption) Flash Memory (Cypress/Spansion) S29GL512S USB Port (mnf. unknown) Flash Memory (Cypress/Spansion) LAN Port MS04G100BHI00 (mnf. unknown) GE Switch (Broadcom) BCM53365 (assumption) Real Time Clock (Dallas) DS1687-3 Application Processor (HiSilicon) Hi1382 (assumption) Unknown (HiSilicon) SA8008 RNIV100 Operational Amplifier (Analog Devices) AD80341 FPGA (Lattice Semiconductor) LCMXO1200C Unknown (HiSilicon) SD5000RBI 200 FPGA (Xilinx) ARTIX-7 XC7A100T Reset Switch (mnf. unknown) M8 GNSS (Ublox) UBX-M8030-KT Serial Flash Memory (Winbond) W25Q16JV (C) Fomalhaut Techno Solutions.