Chap01: Introduction
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The Road Ahead for Computing Systems
56 JANUARY 2019 HiPEAC conference 2019 The road ahead for Valencia computing systems Monica Lam on keeping the web open Alberto Sangiovanni Vincentelli on building tech businesses Koen Bertels on quantum computing Tech talk 2030 contents 7 14 16 Benvinguts a València Monica Lam on open-source Starting and scaling a successful voice assistants tech business 3 Welcome 30 SME snapshot Koen De Bosschere UltraSoC: Smarter systems thanks to self-aware chips 4 Policy corner Rupert Baines The future of technology – looking into the crystal ball 33 Innovation Europe Sandro D’Elia M2DC: The future of modular microserver technology 6 News João Pita Costa, Ariel Oleksiak, Micha vor dem Berge and Mario Porrmann 14 HiPEAC voices 34 Innovation Europe ‘We are witnessing the creation of closed, proprietary TULIPP: High-performance image processing for linguistic webs’ embedded computers Monica Lam Philippe Millet, Diana Göhringer, Michael Grinberg, 16 HiPEAC voices Igor Tchouchenkov, Magnus Jahre, Magnus Peterson, ‘Do not think that SME status is the final game’ Ben Rodriguez, Flemming Christensen and Fabien Marty Alberto Sangiovanni Vincentelli 35 Innovation Europe 18 Technology 2030 Software for the big data era with E2Data Computing for the future? The way forward for Juan Fumero computing systems 36 Innovation Europe Marc Duranton, Madeleine Gray and Marcin Ostasz A RECIPE for HPC success 23 Technology 2030 William Fornaciari Tech talk 2030 37 Innovation Europe Solving heterogeneous challenges with the 24 Future compute special Heterogeneity Alliance -
Fog Computing: a Platform for Internet of Things and Analytics
Fog Computing: A Platform for Internet of Things and Analytics Flavio Bonomi, Rodolfo Milito, Preethi Natarajan and Jiang Zhu Abstract Internet of Things (IoT) brings more than an explosive proliferation of endpoints. It is disruptive in several ways. In this chapter we examine those disrup- tions, and propose a hierarchical distributed architecture that extends from the edge of the network to the core nicknamed Fog Computing. In particular, we pay attention to a new dimension that IoT adds to Big Data and Analytics: a massively distributed number of sources at the edge. 1 Introduction The “pay-as-you-go” Cloud Computing model is an efficient alternative to owning and managing private data centers (DCs) for customers facing Web applications and batch processing. Several factors contribute to the economy of scale of mega DCs: higher predictability of massive aggregation, which allows higher utilization with- out degrading performance; convenient location that takes advantage of inexpensive power; and lower OPEX achieved through the deployment of homogeneous compute, storage, and networking components. Cloud computing frees the enterprise and the end user from the specification of many details. This bliss becomes a problem for latency-sensitive applications, which require nodes in the vicinity to meet their delay requirements. An emerging wave of Internet deployments, most notably the Internet of Things (IoTs), requires mobility support and geo-distribution in addition to location awareness and low latency. We argue that a new platform is needed to meet these requirements; a platform we call Fog Computing [1]. We also claim that rather than cannibalizing Cloud Computing, F. Bonomi R. -
Understanding Performance Numbers in Integrated Circuit Design Oprecomp Summer School 2019, Perugia Italy 5 September 2019
Understanding performance numbers in Integrated Circuit Design Oprecomp summer school 2019, Perugia Italy 5 September 2019 Frank K. G¨urkaynak [email protected] Integrated Systems Laboratory Introduction Cost Design Flow Area Speed Area/Speed Trade-offs Power Conclusions 2/74 Who Am I? Born in Istanbul, Turkey Studied and worked at: Istanbul Technical University, Istanbul, Turkey EPFL, Lausanne, Switzerland Worcester Polytechnic Institute, Worcester MA, USA Since 2008: Integrated Systems Laboratory, ETH Zurich Director, Microelectronics Design Center Senior Scientist, group of Prof. Luca Benini Interests: Digital Integrated Circuits Cryptographic Hardware Design Design Flows for Digital Design Processor Design Open Source Hardware Integrated Systems Laboratory Introduction Cost Design Flow Area Speed Area/Speed Trade-offs Power Conclusions 3/74 What Will We Discuss Today? Introduction Cost Structure of Integrated Circuits (ICs) Measuring performance of ICs Why is it difficult? EDA tools should give us a number Area How do people report area? Is that fair? Speed How fast does my circuit actually work? Power These days much more important, but also much harder to get right Integrated Systems Laboratory The performance establishes the solution space Finally the cost sets a limit to what is possible Introduction Cost Design Flow Area Speed Area/Speed Trade-offs Power Conclusions 4/74 System Design Requirements System Requirements Functionality Functionality determines what the system will do Integrated Systems Laboratory Finally the cost sets a limit -
Chap01: Computer Abstractions and Technology
CHAPTER 1 Computer Abstractions and Technology 1.1 Introduction 3 1.2 Eight Great Ideas in Computer Architecture 11 1.3 Below Your Program 13 1.4 Under the Covers 16 1.5 Technologies for Building Processors and Memory 24 1.6 Performance 28 1.7 The Power Wall 40 1.8 The Sea Change: The Switch from Uniprocessors to Multiprocessors 43 1.9 Real Stuff: Benchmarking the Intel Core i7 46 1.10 Fallacies and Pitfalls 49 1.11 Concluding Remarks 52 1.12 Historical Perspective and Further Reading 54 1.13 Exercises 54 CMPS290 Class Notes (Chap01) Page 1 / 24 by Kuo-pao Yang 1.1 Introduction 3 Modern computer technology requires professionals of every computing specialty to understand both hardware and software. Classes of Computing Applications and Their Characteristics Personal computers o A computer designed for use by an individual, usually incorporating a graphics display, a keyboard, and a mouse. o Personal computers emphasize delivery of good performance to single users at low cost and usually execute third-party software. o This class of computing drove the evolution of many computing technologies, which is only about 35 years old! Server computers o A computer used for running larger programs for multiple users, often simultaneously, and typically accessed only via a network. o Servers are built from the same basic technology as desktop computers, but provide for greater computing, storage, and input/output capacity. Supercomputers o A class of computers with the highest performance and cost o Supercomputers consist of tens of thousands of processors and many terabytes of memory, and cost tens to hundreds of millions of dollars. -
A Dynamic Cloud Computing Platform for Ehealth Systems
A Dynamic Cloud Computing Platform for eHealth Systems Mehdi Bahrami 1 and Mukesh Singhal 2 Cloud Lab University of California Merced, USA Email: 1 IEEE Senior Member, [email protected]; 2 IEEE Fellow, [email protected] Abstract— Cloud Computing technology offers new Application Programming Interface (API) could have some opportunities for outsourcing data, and outsourcing computation issue when the application transfer to a cloud computing system to individuals, start-up businesses, and corporations in health that needs to redefine or modify the security functions of the API care. Although cloud computing paradigm provides interesting, in order to use the cloud. Each cloud computing system offer and cost effective opportunities to the users, it is not mature, and own services to using the cloud introduces new obstacles to users. For instance, vendor lock-in issue that causes a healthcare system rely on a cloud Security Issue: Data security refers to accessibility of stored vendor infrastructure, and it does not allow the system to easily data to only authorized users, and network security refers to transit from one vendor to another. Cloud data privacy is another accessibility of transfer of data between two authorized users issue and data privacy could be violated due to outsourcing data through a network. Since cloud computing uses the Internet as to a cloud computing system, in particular for a healthcare system part of its infrastructure, stored data on a cloud is vulnerable to that archives and processes sensitive data. In this paper, we both a breach in data and network security. present a novel cloud computing platform based on a Service- Oriented cloud architecture. -
In-Memory Computing Platform: Data Grid Deep Dive
In-Memory Computing Platform: Data Grid Deep Dive Rachel Pedreschi Matt Sarrel Director of Solutions Architecture Director of Technical Marketing GridGain Systems GridGain Systems [email protected] [email protected] @rachelpedreschi @msarrel © 2016 GridGain Systems, Inc. GridGain Company Confidential Agenda • Introduction • In-Memory Computing • GridGain / Apache Ignite Overview • Survey Results • Data Grid Deep Dive • Customer Case Studies © 2016 GridGain Systems, Inc. Why In-Memory Now? Digital Transformation is Driving Companies Closer to Their Customers • Driving a need for real-time interactions Internet Traffic, Data, and Connected Devices Continue to Grow • Web-scale applications and massive datasets require in-memory computing to scale out and speed up to keep pace • The Internet of Things generates huge amounts of data which require real-time analysis for real world uses The Cost of RAM Continues to Fall • In-memory solutions are increasingly cost effective versus disk-based storage for many use cases © 2015 GridGain Systems, Inc. GridGain Company Confidential Why Now? Data Growth and Internet Scale Declining DRAM Cost Driving Demand Driving Attractive Economics Growth of Global Data 35. 26.3 17.5 ZettabytesData of 8.8 DRAM 0. Flash Disk 8 zettabytes in 2015 growing to 35 in 2020 Cost drops 30% every 12 months © 2016 GridGain Systems, Inc. The In-Memory Computing Technology Market Is Big — And Growing Rapidly IMC-Enabling Application Infrastructure ($M) © 2016 GridGain Systems, Inc. What is an In-Memory Computing Platform? -
Photovoltaic Couplers for MOSFET Drive for Relays
Photocoupler Application Notes Basic Electrical Characteristics and Application Circuit Design of Photovoltaic Couplers for MOSFET Drive for Relays Outline: Photovoltaic-output photocouplers(photovoltaic couplers), which incorporate a photodiode array as an output device, are commonly used in combination with a discrete MOSFET(s) to form a semiconductor relay. This application note discusses the electrical characteristics and application circuits of photovoltaic-output photocouplers. ©2019 1 Rev. 1.0 2019-04-25 Toshiba Electronic Devices & Storage Corporation Photocoupler Application Notes Table of Contents 1. What is a photovoltaic-output photocoupler? ............................................................ 3 1.1 Structure of a photovoltaic-output photocoupler .................................................... 3 1.2 Principle of operation of a photovoltaic-output photocoupler .................................... 3 1.3 Basic usage of photovoltaic-output photocouplers .................................................. 4 1.4 Advantages of PV+MOSFET combinations ............................................................. 5 1.5 Types of photovoltaic-output photocouplers .......................................................... 7 2. Major electrical characteristics and behavior of photovoltaic-output photocouplers ........ 8 2.1 VOC-IF characteristics .......................................................................................... 9 2.2 VOC-Ta characteristic ........................................................................................ -
COSC 6385 Computer Architecture - Multi-Processors (IV) Simultaneous Multi-Threading and Multi-Core Processors Edgar Gabriel Spring 2011
COSC 6385 Computer Architecture - Multi-Processors (IV) Simultaneous multi-threading and multi-core processors Edgar Gabriel Spring 2011 Edgar Gabriel Moore’s Law • Long-term trend on the number of transistor per integrated circuit • Number of transistors double every ~18 month Source: http://en.wikipedia.org/wki/Images:Moores_law.svg COSC 6385 – Computer Architecture Edgar Gabriel 1 What do we do with that many transistors? • Optimizing the execution of a single instruction stream through – Pipelining • Overlap the execution of multiple instructions • Example: all RISC architectures; Intel x86 underneath the hood – Out-of-order execution: • Allow instructions to overtake each other in accordance with code dependencies (RAW, WAW, WAR) • Example: all commercial processors (Intel, AMD, IBM, SUN) – Branch prediction and speculative execution: • Reduce the number of stall cycles due to unresolved branches • Example: (nearly) all commercial processors COSC 6385 – Computer Architecture Edgar Gabriel What do we do with that many transistors? (II) – Multi-issue processors: • Allow multiple instructions to start execution per clock cycle • Superscalar (Intel x86, AMD, …) vs. VLIW architectures – VLIW/EPIC architectures: • Allow compilers to indicate independent instructions per issue packet • Example: Intel Itanium series – Vector units: • Allow for the efficient expression and execution of vector operations • Example: SSE, SSE2, SSE3, SSE4 instructions COSC 6385 – Computer Architecture Edgar Gabriel 2 Limitations of optimizing a single instruction -
The FPGA As a Computing Platform
X01_Introduction.qxp 3/15/2005 3:31 PM Page 1 CHAPTER 1 The FPGA as a Computing Platform As the cost per gate of FPGAs declines, embedded and high-performance sys- tems designers are being presented with new opportunities for creating accel- erated software applications using FPGA-based programmable hardware platforms. From a hardware perspective, these new platforms effectively bridge the gap between software programmable systems based on traditional microprocessors, and application-specific platforms based on custom hard- ware functions. From a software perspective, advances in design tools and methodology for FPGA-based platforms enable the rapid creation of hardware-accelerated algorithms. The opportunities presented by these programmable hardware plat- forms include creation of custom hardware functions by software engineers, later design freeze dates, simplified field updates, and the reduction or elimi- nation of custom chips from many categories of electronic products. Increas- ingly, systems designers are seeing the benefits of using FPGAs as the basis for applications that are traditionally in the domain of application-specific in- tegrated circuits (ASICs). As FPGAs have grown in logic capacity, their ability to host high-performance software algorithms and complete applications has grown correspondingly. In this chapter, we will present a brief overview of FPGAs and FPGA- based platforms and present the general philosophy behind using the C lan- guage for FPGA application development. Experienced FPGA users will find 1 X01_Introduction.qxp 3/15/2005 3:31 PM Page 2 2 The FPGA as a Computing Platform much of this information familiar, but nonetheless we hope you stay with us as we take the FPGA into new, perhaps unfamiliar territory: that of high-performance computing. -
Computing Platforms Chapter 4
Computing Platforms Chapter 4 COE 306: Introduction to Embedded Systems Dr. Abdulaziz Tabbakh Computer Engineering Department College of Computer Sciences and Engineering King Fahd University of Petroleum and Minerals [Adapted from slides of Dr. A. El-Maleh, COE 306, KFUPM] Next . Basic Computing Platforms The CPU bus Direct Memory Access (DMA) System Bus Configurations ARM Bus: AMBA 2.0 Memory Components Embedded Platforms Platform-Level Performance Computing Platforms COE 306– Introduction to Embedded System– KFUPM slide 2 Embedded Systems Overview Actuator Output Analog/Digital Sensor Input Analog/Digital CPU Memory Embedded Computer Computing Platforms COE 306– Introduction to Embedded System– KFUPM slide 3 Computing Platforms Computing platforms are created using microprocessors, I/O devices, and memory components A CPU bus is required to connect the CPU to other devices Software is required to implement an application Embedded system software is closely tied to the hardware Computing Platform: hardware and software Computing Platforms COE 306– Introduction to Embedded System– KFUPM slide 4 Computing Platform A typical computing platform includes several major hardware components: The CPU provides basic computational facilities. RAM is used for program and data storage. ROM holds the boot program and some permanent data. A DMA controller provides direct memory access capabilities. Timers are used by the operating system A high-speed bus, connected to the CPU bus through a bridge, allows fast devices to communicate efficiently with the rest of the system. A low-speed bus provides an inexpensive way to connect simpler devices and may be necessary for backward compatibility as well. Computing Platforms COE 306– Introduction to Embedded System– KFUPM slide 5 Platform Hardware Components Computer systems may have one or more bus Buses are classified by their overall performance: lows peed, high- speed. -
Rapid Mapping of Digital Integrated Circuit Logic Gates Via Multi-Spectral Backside Imaging
RAPID MAPPING OF DIGITAL INTEGRATED CIRCUIT LOGIC GATES VIA MULTI-SPECTRAL BACKSIDE IMAGING RONEN ADATO1;4;∗, AYDAN UYAR1;4, MAHMOUD ZANGENEH1;4, BOYOU ZHOU1;4, AJAY JOSHI1;4, BENNETT GOLDBERG1;2;3;4 AND M SELIM UNL¨ U¨ 1;3;4 Abstract. Modern semiconductor integrated circuits are increasingly fabricated at untrusted third party foundries. There now exist myriad security threats of malicious tampering at the hardware level and hence a clear and pressing need for new tools that enable rapid, robust and low-cost validation of circuit layouts. Optical backside imaging offers an attractive platform, but its limited resolution and throughput cannot cope with the nanoscale sizes of modern circuitry and the need to image over a large area. We propose and demonstrate a multi-spectral imaging approach to overcome these obstacles by identifying key circuit elements on the basis of their spectral response. This obviates the need to directly image the nanoscale components that define them, thereby relaxing resolution and spatial sampling requirements by 1 and 2 - 4 orders of magnitude respectively. Our results directly address critical security needs in the integrated circuit supply chain and highlight the potential of spectroscopic techniques to address fundamental resolution obstacles caused by the need to image ever shrinking feature sizes in semiconductor integrated circuits. 1. Introduction Semiconductor integrated circuits (ICs) are pervasive and essential components in virtually all modern devices, from personal computers, to medical equipment, to varied military systems and technologies. Their functionality is defined by a massive number (∼ 106 − 109 currently) of in- terconnected logic gates that correspond physically to various nanoscale doped regions, polysilicon and metal (usually copper and tungsten) structures. -
Download from an Unknown Website Cannot Be Said to Be ‘Safe’ Just Because It Happens Not to Harbor a Virus
A direct path to dependable software The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Jackson, Daniel. “A direct path to dependable software.” Commun. ACM 52.4 (2009): 78-88. As Published http://dx.doi.org/10.1145/1498765.1498787 Publisher Association for Computing Machinery Version Author's final manuscript Citable link http://hdl.handle.net/1721.1/51683 Terms of Use Attribution-Noncommercial-Share Alike 3.0 Unported Detailed Terms http://creativecommons.org/licenses/by-nc-sa/3.0/ A Direct Path To Dependable Software Daniel Jackson Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology What would it take to make software more dependable? Until now, most approaches have been indirect: some practices – processes, tools or techniques – are used that are believed to yield dependable software, and the argument for dependability rests on the extent to which the developers have adhered to them. This article argues instead that developers should produce direct evidence that the software satisfies its dependability claims. The potential advantages of this approach are greater credibility (since the ar- gument is not contingent on the effectiveness of the practices) and reduced cost (since development resources can be focused where they have the most impact). 1 Why We Need Better Evidence Is a system that never fails dependable? Not necessarily. A dependable system is one you can depend on – that is, in which you can place your reliance or trust. A rational person or organization only does this with evidence that the system’s benefits outweigh its risks.