China's Bid to Lead Artificial Intelligence Chip Development Within the Decade
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GPU Developments 2018
GPU Developments 2018 2018 GPU Developments 2018 © Copyright Jon Peddie Research 2019. 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. -
Network on Chip for FPGA Development of a Test System for Network on Chip
Network on Chip for FPGA Development of a test system for Network on Chip Magnus Krokum Namork Master of Science in Electronics Submission date: June 2011 Supervisor: Kjetil Svarstad, IET Norwegian University of Science and Technology Department of Electronics and Telecommunications Problem Description This assignment is a continuation of the project-assignment of fall 2010, where it was looked into the development of reactive modules for application-test and profiling of the Network on Chip realization. It will especially be focused on the further development of: • The programmability of the system by developing functionality for more advanced surveillance of the communication between modules and routers • Framework that will be used to test and profile entire applications on the Network on Chip The work will primarily be directed towards testing and running the system in a way that resembles a real system at run time. The work is to be compared with relevant research within similar work. Assignment given: January 2011 Supervisor: Kjetil Svarstad, IET i Abstract Testing and verification of digital systems is an essential part of product develop- ment. The Network on Chip (NoC), as a new paradigm within interconnections; has a specific need for testing. This is to determine how performance and prop- erties of the NoC are compared to the requirements of different systems such as processors or media applications. A NoC has been developed within the AHEAD project to form a basis for a reconfigurable platform used in the AHEAD system. This report gives an outline of the project to develop testing and benchmarking systems for a NoC. -
ESE 150 – Tasks? DIGITAL AUDIO BASICS ESE150 Spring 2021 Based on Slides © 2009--2021 Dehon 1 2
3/31/21 ESE150 Spring 2021 OBSERVATION Ò We want our phones (and computers) to do many things at once. Ò If we dedicate a processor to MP3 decoding É It will sit idle most of the time Ò MP3 decoding (and many other things) do not consume a modern processor Lecture #18 – Operating Systems (OS) Ò Idea: Maybe we can share the processor among ESE 150 – tasks? DIGITAL AUDIO BASICS ESE150 Spring 2021 Based on slides © 2009--2021 DeHon 1 2 ESE150 Spring 2021 ESE150 Spring 2021 BIG IDEA OUTLINE Ò Provide the illusion of (virtually) unlimited Ò Review processors by sharing single processor in time Ò Worksheet: Virtualization In Action Ò Strategy É Time-share processor Ð Store all process (virtual processors) state in memory Ð Iterate through processes × Restore process state × Run for a number of cycles × Save process state 3 4 ESE150 Spring 2021 ESE150 Spring 2021 COURSE MAP – WEEK 10 ROLE OF OPERATING SYSTEM MIC Ò Higher-level, shared support for all programs A/D É Could put it in program, but most programs need it! É Needs to be abstracted from program Music 1 domain conversion 5,6 compress Ò ResourCe sharing Numbers É Processor, memory, “devices” (net, printer, audio) correspond to pyscho- course weeks sample freq Ò acoustics 3 Polite sharing 2 4 É Isolation and protection EULA É Fences make Good Neighbors – R. Frost -------- D/A 10101001101 -------- Ò Idea: Expensive/limited resources can be shared in click time – OS manages tHis sHaring OK speaker MP3 Player / iPhone / Droid 5 6 1 3/31/21 ESE150 Spring 2021 ESE150 Spring 2021 IDEA Ò Virtualize -
Sensors and Data Encryption, Two Aspects of Electronics That Used to Be Two Worlds Apart and That Are Now Often Tightly Integrated, One Relying on the Other
www.eenewseurope.com January 2019 electronics europe News News e-skin beats human touch Swedish startup beats e-Ink on low-power Special Focus: Power Sources european business press November 2011 Electronic Engineering Times Europe1 181231_8-3_Mill_EENE_EU_Snipe.indd 1 12/14/18 3:59 PM 181231_QualR_EENE_EU.indd 1 12/14/18 3:53 PM CONTENTS JANUARY 2019 Dear readers, www.eenewseurope.com January 2019 The Consumer Electronics Show has just closed its doors in Las Vegas, yet the show has opened the mind of many designers, some returning home with new electronics europe News News ideas and possibly new companies to be founded. All the electronic devices unveiled at CES share in common the need for a cheap power source and a lot of research goes into making power sources more sus- tainable. While lithium-ion batteries are commercially mature, their long-term viability is often questioned and new battery chemistries are being investigated for their simpler material sourcing, lower cost and sometime increased energy density. Energy harvesting is another feature that is more and more often inte- e-skin beats human touch grated into wearables but also at grid-level. Our Power Sources feature will give you a market insight and reviews some of the latest findings. Other topics covered in our January edition are Sensors and Data Encryption, two aspects of electronics that used to be two worlds apart and that are now often tightly integrated, one relying on the other. Swedish startup beats e-Ink on low-power With this first edition of 2019, let me wish you all an excellent year and plenty Special Focus: Power Sources of new business opportunities, whether you are designing the future for a european business press startup or working for a well-established company. -
Embedded Networks on Chip for Field-Programmable Gate Arrays by Mohamed Saied Abdelfattah a Thesis Submitted in Conformity With
Embedded Networks on Chip for Field-Programmable Gate Arrays by Mohamed Saied Abdelfattah A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Electrical and Computer Engineering University of Toronto © Copyright 2016 by Mohamed Saied Abdelfattah Abstract Embedded Networks on Chip for Field-Programmable Gate Arrays Mohamed Saied Abdelfattah Doctor of Philosophy Graduate Department of Electrical and Computer Engineering University of Toronto 2016 Modern field-programmable gate arrays (FPGAs) have a large capacity and a myriad of embedded blocks for computation, memory and I/O interfacing. This allows the implementation of ever-larger applications; however, the increase in application size comes with an inevitable increase in complexity, making it a challenge to implement on-chip communication. Today, it is a designer's burden to create a customized communication circuit to interconnect an application, using the fine-grained FPGA fab- ric that has single-bit control over every wire segment and logic cell. Instead, we propose embedding a network-on-chip (NoC) to implement system-level communication on FPGAs. A prefabricated NoC improves communication efficiency, eases timing closure, and abstracts system-level communication on FPGAs, separating an application's behaviour and communication which makes the design of complex FPGA applications easier and faster. This thesis presents a complete embedded NoC solution, includ- ing the NoC architecture and interface, rules to guide its use with FPGA design styles, application case studies to showcase its advantages, and a computer-aided design (CAD) system to automatically interconnect applications using an embedded NoC. We compare NoC components when implemented hard versus soft, then build on this component-level analysis to architect embedded NoCs and integrate them into the FPGA fabric; these NoCs are on average 20{23× smaller and 5{6× faster than soft NoCs. -
An Early Performance Evaluation of Many Integrated Core Architecture Based SGI Rackable Computing System
NAS Technical Report: NAS-2015-04 An Early Performance Evaluation of Many Integrated Core Architecture Based SGI Rackable Computing System Subhash Saini,1 Haoqiang Jin,1 Dennis Jespersen,1 Huiyu Feng,2 Jahed Djomehri,3 William Arasin,3 Robert Hood,3 Piyush Mehrotra,1 Rupak Biswas1 1NASA Ames Research Center 2SGI 3Computer Sciences Corporation Moffett Field, CA 94035-1000, USA Fremont, CA 94538, USA Moffett Field, CA 94035-1000, USA {subhash.saini, haoqiang.jin, [email protected] {jahed.djomehri, william.f.arasin, dennis.jespersen, piyush.mehrotra, robert.hood}@nasa.gov rupak.biswas}@nasa.gov ABSTRACT The GPGPU approach relies on streaming multiprocessors and Intel recently introduced the Xeon Phi coprocessor based on the uses a low-level programming model such as CUDA or a high- Many Integrated Core architecture featuring 60 cores with a peak level programming model like OpenACC to attain high performance of 1.0 Tflop/s. NASA has deployed a 128-node SGI performance [1-3]. Intel’s approach has a Phi serving as a Rackable system where each node has two Intel Xeon E2670 8- coprocessor to a traditional Intel processor host. The Phi has x86- core Sandy Bridge processors along with two Xeon Phi 5110P compatible cores with wide vector processing units and uses coprocessors. We have conducted an early performance standard parallel programming models such as MPI, OpenMP, evaluation of the Xeon Phi. We used microbenchmarks to hybrid (MPI + OpenMP), UPC, etc. [4-5]. measure the latency and bandwidth of memory and interconnect, Understanding performance of these heterogeneous computing I/O rates, and the performance of OpenMP directives and MPI systems has become very important as they have begun to appear functions. -
A Comparison of Four Series of Cisco Network Processors
Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.4, October 2016 A COMPARISON OF FOUR SERIES OF CISCO NETWORK PROCESSORS Sadaf Abaei Senejani 1, Hossein Karimi 2 and Javad Rahnama 3 1Department of Computer Engineering, Pooyesh University, Qom, Iran 2 Sama Technical and Vocational Training College, Islamic Azad University, Yasouj Branch, Yasouj, Iran 3 Department of Computer Science and Engineering, Sharif University of Technology, Tehran, Iran. ABSTRACT Network processors have created new opportunities by performing more complex calculations. Routers perform the most useful and difficult processing operations. In this paper the routers of VXR 7200, ISR 4451-X, SBC 7600, 7606 have been investigated which their main positive points include scalability, flexibility, providing integrated services, high security, supporting and updating with the lowest cost, and supporting standard protocols of network. In addition, in the current study these routers have been explored from hardware and processor capacity viewpoints separately. KEYWORDS Network processors, Network on Chip, parallel computing 1. INTRODUCTION The expansion of science and different application fields which require complex and time taking calculations and in consequence the demand for fastest and more powerful computers have made the need for better structures and new methods more significant. This need can be met through solutions called “paralleled massive computers” or another solution called “clusters of computers”. It should be mentioned that both solutions depend highly on intercommunication networks with high efficiency and appropriate operational capacity. Therefore, as a solution combining numerous cores on a single chip via using Network on chip technology for the simplification of communications can be done. -
MYTHIC MULTIPLIES in a FLASH Analog In-Memory Computing Eliminates DRAM Read/Write Cycles
MYTHIC MULTIPLIES IN A FLASH Analog In-Memory Computing Eliminates DRAM Read/Write Cycles By Mike Demler (August 27, 2018) ................................................................................................................... Machine learning is a hot field that attracts high-tech The startup plans by the end of this year to tape out investors, causing the number of startups to explode. As its first product, which can store up to 50 million weights. these young companies strive to compete against much larg- At that time, it also aims to release the alpha version of its er established players, some are revisiting technologies the software tools and a performance profiler. The company veterans may have dismissed, such as analog computing. expects to begin volume shipments in 4Q19. Mythic is riding this wave, using embedded flash-memory technology to store neural-network weights as analog pa- Solving a Weighty Problem rameters—an approach that eliminates the power consumed Mythic uses Fujitsu’s 40nm embedded-flash cell, which it in moving data between the processor and DRAM. plans to integrate in an array along with digital-to-analog The company traces its origin to 2012 at the University converters (DACs) and analog-to-digital converters (ADCs), of Michigan, where CTO Dave Fick completed his doctoral as Figure 1 shows. By storing a range of analog voltages in degree and CEO Mike Henry spent two and a half years as the bit cells, the technique uses the cell’s voltage-variable a visiting scholar. The founders worked on a DoD-funded conductance to represent weights with 8-bit resolution. This project to develop machine-learning-powered surveillance drones, eventually leading to the creation of their startup. -
Hardware-Assisted Rootkits: Abusing Performance Counters on the ARM and X86 Architectures
Hardware-Assisted Rootkits: Abusing Performance Counters on the ARM and x86 Architectures Matt Spisak Endgame, Inc. [email protected] Abstract the OS. With KPP in place, attackers are often forced to move malicious code to less privileged user-mode, to ele- In this paper, a novel hardware-assisted rootkit is intro- vate privileges enabling a hypervisor or TrustZone based duced, which leverages the performance monitoring unit rootkit, or to become more creative in their approach to (PMU) of a CPU. By configuring hardware performance achieving a kernel mode rootkit. counters to count specific architectural events, this re- Early advances in rootkit design focused on low-level search effort proves it is possible to transparently trap hooks to system calls and interrupts within the kernel. system calls and other interrupts driven entirely by the With the introduction of hardware virtualization exten- PMU. This offers an attacker the opportunity to redirect sions, hypervisor based rootkits became a popular area control flow to malicious code without requiring modifi- of study allowing malicious code to run underneath a cations to a kernel image. guest operating system [4, 5]. Another class of OS ag- The approach is demonstrated as a kernel-mode nostic rootkits also emerged that run in System Manage- rootkit on both the ARM and Intel x86-64 architectures ment Mode (SMM) on x86 [6] or within ARM Trust- that is capable of intercepting system calls while evad- Zone [7]. The latter two categories, which leverage vir- ing current kernel patch protection implementations such tualization extensions, SMM on x86, and security exten- as PatchGuard. -
Product Datasheet [email protected]
0800 064 64 64 Product Datasheet [email protected] Apple iPhone 11 Pro Specification BATTERY IP RATING Battery: 3,046 mAh IP Rating: IP68 CAMERA MEMORY Main Camera: 12MP / 12MP / 12MP Memory: Internal Selfie Camera: 12MP MEMORY TYPE CHARGING 64 GB 4 GB RAM / 256 GB 4 Memory Type: GB RAM / 512 GB 4GB RAM Wireless Yes Charging: Then there was Pro. Pro Cameras. Pro Charging 2.0, proprietary reversible OS display. Pro performance. Type: connector iOS 13, upgradable to iOS OS: Fast Charging: Yes 13.3 A transformative triple-camera system that adds tons of capability without CHIPSET SIM TYPE complexity. An unprecedented leap in Chipset: Apple A13 Bionic (7 nm+) Sim Type: Nano-SIM and/or eSIM battery life. And a mind-blowing chip that elevates machine learning and pushes COLOURS SOUND the boundaries. Welcome to the first Contact your Account Colours: Sound: Yes iPhone powerful enough to be called Pro. Manager The iPhone 11 Pro 5.8” (diagonal) all- TECHNOLOGY CONNECTIVITY screen OLED Super Retina XDR Display GSM / CDMA / HSPA / Technology: makes it easy to view documents, read Wi-Fi: Yes EVDO / LTE emails and brings videos to life. GPS: Yes NFC: Yes VIDEO iPhone 11 Pro's Ultra-Wide Bluetooth: 5.0, A2DP, LE 2160p@24/30/60fps, camera shows users what’s happening Video: 1080p@30/60/120fps, gyro- outside the frame — and lets them CPU EIS capture it. Unleash creativity or capture Hexa-core (2x2.65 GHz high-quality video for that any occasion. CPU: Lightning + 4x1.8 GHz DIMENSIONS AND WEIGHT Thunder) Shoots sharp 4K video with cinematic Weight: 188g features. -
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. -
Demand-Based Scheduling Using Noc Probes
Demand-based Scheduling using NoC Probes Kurt Shuler Jonah Probell Monica Tang Arteris, Inc. Sunnyvale, CA, USA www.arteris.com ABSTRACT Contention for shared resources, such as memory in a system-on-chip, is inefficient. It limits performance and requires initiator IPs, such as CPUs, to stay powered up and run at clock speeds higher than they would otherwise. When a system runs multiple tasks simultaneously, contention will vary depending on each task’s demand for shared resources. Scheduling in a conventional system does not consider how much each task will demand shared resources. Proposed is a demand-based method of scheduling tasks that have diverse demand for shared resources in a multi- processor system-on-chip. Tasks with extremes of high and low demand are scheduled to run simultaneously. Where tasks in a system are cyclical, knowledge of period, duty cycle, phase, and magnitude are considered in the scheduling algorithm. Using demand-based scheduling, the time variation of aggregate demand is reduced, as might be its maximum. The average access latency and resulting idle clock cycles are reduced. This allows initiator IPs to run at lower clock frequencies, finish their tasks sooner, and spend more time in powered down modes. Use of probes within a network-on-chip to gather demand statistics, application-specific algorithm considerations, operating system thread scheduler integration, and heterogeneous system middleware integration are discussed. Contents 1. Introduction ......................................................................................................................................................................