ARM Processor Modeling at a Cycle Accurate Level in Systemc
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												  Simics* Model Library for Eagle Stream Simulation EnvironmentSimics* Model Library for Eagle Stream Simulation Environment Release Notes May 2020 Revision V0.6.00 Intel Confidential Notice: This document contains information on products in the design phase of development. The information here is subject to change without notice. Do not finalize a design with this information. Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software, or service activation. Learn more at intel.com, or from the OEM or retailer. No computer system can be absolutely secure. Intel does not assume any liability for lost or stolen data or systems or any damages resulting from such losses. You may not use or facilitate the use of this document in connection with any infringement or other legal analysis concerning Intel products described herein. You agree to grant Intel a non-exclusive, royalty-free license to any patent claim thereafter drafted which includes subject matter disclosed herein. No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document. The products described may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request. This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest Intel product specifications and roadmaps. Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade.
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												  45-Year CPU Evolution: One Law and Two Equations45-year CPU evolution: one law and two equations Daniel Etiemble LRI-CNRS University Paris Sud Orsay, France [email protected] Abstract— Moore’s law and two equations allow to explain the a) IC is the instruction count. main trends of CPU evolution since MOS technologies have been b) CPI is the clock cycles per instruction and IPC = 1/CPI is the used to implement microprocessors. Instruction count per clock cycle. c) Tc is the clock cycle time and F=1/Tc is the clock frequency. Keywords—Moore’s law, execution time, CM0S power dissipation. The Power dissipation of CMOS circuits is the second I. INTRODUCTION equation (2). CMOS power dissipation is decomposed into static and dynamic powers. For dynamic power, Vdd is the power A new era started when MOS technologies were used to supply, F is the clock frequency, ΣCi is the sum of gate and build microprocessors. After pMOS (Intel 4004 in 1971) and interconnection capacitances and α is the average percentage of nMOS (Intel 8080 in 1974), CMOS became quickly the leading switching capacitances: α is the activity factor of the overall technology, used by Intel since 1985 with 80386 CPU. circuit MOS technologies obey an empirical law, stated in 1965 and 2 Pd = Pdstatic + α x ΣCi x Vdd x F (2) known as Moore’s law: the number of transistors integrated on a chip doubles every N months. Fig. 1 presents the evolution for II. CONSEQUENCES OF MOORE LAW DRAM memories, processors (MPU) and three types of read- only memories [1]. The growth rate decreases with years, from A.
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												  Cuda C Best Practices GuideCUDA C BEST PRACTICES GUIDE DG-05603-001_v9.0 | June 2018 Design Guide TABLE OF CONTENTS Preface............................................................................................................ vii What Is This Document?..................................................................................... vii Who Should Read This Guide?...............................................................................vii Assess, Parallelize, Optimize, Deploy.....................................................................viii Assess........................................................................................................ viii Parallelize.................................................................................................... ix Optimize...................................................................................................... ix Deploy.........................................................................................................ix Recommendations and Best Practices.......................................................................x Chapter 1. Assessing Your Application.......................................................................1 Chapter 2. Heterogeneous Computing.......................................................................2 2.1. Differences between Host and Device................................................................ 2 2.2. What Runs on a CUDA-Enabled Device?...............................................................3 Chapter 3. Application Profiling.............................................................................
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												  Portable Stimulus: What's Coming in 1.1Portable Stimulus: What’s Coming in 1.1 and What it Means For You Portable Stimulus Working Group PSS 1.1 Tutorial Agenda • What is PSS Introduction • Tom Fitzpatrick, • Abstract DMA model in PSS 1.0 Mentor, a Siemens Business Memory • The problem • Prabhat Gupta, AMD Allocation • New PSS concepts Higher-Level • The problem • Matan Vax, Scenarios • New constructs Cadence Design Systems • The problem • Karthick Gururaj, HSI Realization • New concepts and constructs Vayavya Labs System-Level • Portability • Hillel Miller, Synopsys Usage • Complex scenarios • Summary Special Thanks to: Conclusion • What’s next Dave Kelf, Breker Verification Systems Josh Rensch, Semifore 2 The Need for Verification Abstraction Test content authoring represents major proportion of development SIMULATION Disconnected cross-process methods Test Content • Block EMULATION • UVM tests laborious, error-prone • SoC FPGA PROTO • Hard to hit corner-cases with C tests • Post-Silicon • Disconnected diagnostic creation IP BLOCK SUBSYSTEM FULL SYSTEM Test portability, reuse, scaling, maintenance all problematic 3 Key Aspects of Portable Stimulus Capture pure Partial scenario Composable Formal Automated test Target multiple test intent description scenarios representation generation platforms of test space Separate test intent from implementation High-coverage test generation across the verification process with much less effort 4 PSS Improves Individual Verification Phases IP BLOCK SUB-SYSTEM FULL SYSTEM Create block-level (UVM) tests & Easily model system-level Generate
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												  Arm Cortex-M System Design Kit Technical Reference ManualArm® Cortex®-M System Design Kit Revision: r1p1 Technical Reference Manual Copyright © 2011, 2013, 2017 Arm Limited (or its affiliates). All rights reserved. ARM DDI 0479D (ID110617) Arm Cortex-M System Design Kit Technical Reference Manual Copyright © 2011, 2013, 2017 Arm Limited (or its affiliates). All rights reserved. Release Information The following changes have been made to this document: Change history Date Issue Confidentiality Change 14 March 2011 A Non-Confidential First release for r0p0 16 June 2011 B Non-Confidential Second release for r0p0 19 April 2013 C Non-Confidential First release for r1p0 31 October 2017 D Non-Confidential First release for r1p1 Proprietary Notice This document is protected by copyright and other related rights and the practice or implementation of the information contained in this document may be protected by one or more patents or pending patent applications. No part of this document may be reproduced in any form by any means without the express prior written permission of Arm. No license, express or implied, by estoppel or otherwise to any intellectual property rights is granted by this document unless specifically stated. Your access to the information in this document is conditional upon your acceptance that you will not use or permit others to use the information for the purposes of determining whether implementations infringe any third party patents. THIS DOCUMENT IS PROVIDED “AS IS”. ARM PROVIDES NO REPRESENTATIONS AND NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, INCLUDING, WITHOUT LIMITATION, THE IMPLIED WARRANTIES OF MERCHANTABILITY, SATISFACTORY QUALITY, NON-INFRINGEMENT OR FITNESS FOR A PARTICULAR PURPOSE WITH RESPECT TO THE DOCUMENT.
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												  Workshop on Post-Silicon Debug: Technologies, Methodologies, and Best PracticesWisam Kadry – IBM Research, Haifa 7 June 2012 Workshop on Post-silicon Debug: Technologies, Methodologies, and Best Practices © 2012 IBM Corporation DAC 2012, Post-silicon Debug Workshop Thanks to Mr. Amir Nahir IBM Research – Haifa, Israel Received his BSc in computer science from Technion, IIT in 2005, and is currently pursuing his PhD there. He has been a research staff member at the IBM Research Labs in Haifa since 2006, and has spent most of his time leading the development of Threadmill – a post-silicon functional validation exerciser. Since the beginning of 2011, Amir manages the Post-Silicon Validation and Design Automation Group 2 © 2012 IBM Corporation DAC 2012, Post-silicon Debug Workshop Agenda Session I (09:00-10:30) Wisam Kadry - IBM Haifa Research Lab., Haifa, Israel Kevin Reick - IBM Corp., Austin, TX Subhasish Mitra - Stanford Univ., Stanford, CA David Erikson - Advanced Micro Devices, Fort Collins, CO Bradley Quinton - Tektronix, Inc., Vancouver, BC, Canada Break (10:30-11:00) Session II (11:00-12:30) Alan Hu - Univ. of British Columbia, Vancouver, BC, Canada Keshavan Tiruvallur - Intel Corp., Portland, OR Nagib Hakim - Intel Corp., Santa Clara, CA Valeria Bertacco - Univ. of Michigan, Ann Arbor, MI Sharad Kumar - Freescale Semiconductor, Inc., Noida, India Lunch (12:30-13:30) Panel (13:30-15:00) Moderator: Harry Foster - Mentor Graphics Corp., Plano, TX 3 © 2012 IBM Corporation DAC 2012, Post-silicon Debug Workshop More complex chips 4 © 2012 IBM Corporation DAC 2012, Post-silicon Debug Workshop Observe 5 © 2012 IBM
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												  Multi-Cycle DatapathoperationBook's Definition of Performance • For some program running on machine X, PerformanceX = 1 / Execution timeX • "X is n times faster than Y" PerformanceX / PerformanceY = n • Problem: – machine A runs a program in 20 seconds – machine B runs the same program in 25 seconds 1 Example • Our favorite program runs in 10 seconds on computer A, which hasa 400 Mhz. clock. We are trying to help a computer designer build a new machine B, that will run this program in 6 seconds. The designer can use new (or perhaps more expensive) technology to substantially increase the clock rate, but has informed us that this increase will affect the rest of the CPU design, causing machine B to require 1.2 times as many clockcycles as machine A for the same program. What clock rate should we tellthe designer to target?" • Don't Panic, can easily work this out from basic principles 2 Now that we understand cycles • A given program will require – some number of instructions (machine instructions) – some number of cycles – some number of seconds • We have a vocabulary that relates these quantities: – cycle time (seconds per cycle) – clock rate (cycles per second) – CPI (cycles per instruction) a floating point intensive application might have a higher CPI – MIPS (millions of instructions per second) this would be higher for a program using simple instructions 3 Performance • Performance is determined by execution time • Do any of the other variables equal performance? – # of cycles to execute program? – # of instructions in program? – # of cycles per second? – average # of cycles per instruction? – average # of instructions per second? • Common pitfall: thinking one of the variables is indicative of performance when it really isn’t.
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												  CS2504: Computer OrganizationCS2504, Spring'2007 ©Dimitris Nikolopoulos CS2504: Computer Organization Lecture 4: Evaluating Performance Instructor: Dimitris Nikolopoulos Guest Lecturer: Matthew Curtis-Maury CS2504, Spring'2007 ©Dimitris Nikolopoulos Understanding Performance Why do we study performance? Evaluate during design Evaluate before purchasing Key to understanding underlying organizational motivation How can we (meaningfully) compare two machines? Performance, Cost, Value, etc Main issue: Need to understand what factors in the architecture contribute to overall system performance and the relative importance of these factors Effects of ISA on performance 2 How will hardware change affect performance CS2504, Spring'2007 ©Dimitris Nikolopoulos Airplane Performance Analogy Airplane Passengers Range Speed Boeing 777 375 4630 610 Boeing 747 470 4150 610 Concorde 132 4000 1250 Douglas DC-8-50 146 8720 544 Fighter Jet 4 2000 1500 What metric do we use? Concorde is 2.05 times faster than the 747 747 has 1.74 times higher throughput What about cost? And the winner is: It Depends! 3 CS2504, Spring'2007 ©Dimitris Nikolopoulos Throughput vs. Response Time Response Time: Execution time (e.g. seconds or clock ticks) How long does the program take to execute? How long do I have to wait for a result? Throughput: Rate of completion (e.g. results per second/tick) What is the average execution time of the program? Measure of total work done Upgrading to a newer processor will improve: response time Adding processors to the system will improve: throughput 4 CS2504, Spring'2007 ©Dimitris Nikolopoulos Example: Throughput vs. Response Time Suppose we know that an application that uses both a desktop client and a remote server is limited by network performance.
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												  Analysis of Body Bias Control Using Overhead Conditions for Real Time Systems: a Practical Approach∗IEICE TRANS. INF. & SYST., VOL.E101–D, NO.4 APRIL 2018 1116 PAPER Analysis of Body Bias Control Using Overhead Conditions for Real Time Systems: A Practical Approach∗ Carlos Cesar CORTES TORRES†a), Nonmember, Hayate OKUHARA†, Student Member, Nobuyuki YAMASAKI†, Member, and Hideharu AMANO†, Fellow SUMMARY In the past decade, real-time systems (RTSs), which must in RTSs. These techniques can improve energy efficiency; maintain time constraints to avoid catastrophic consequences, have been however, they often require a large amount of power since widely introduced into various embedded systems and Internet of Things they must control the supply voltages of the systems. (IoTs). The RTSs are required to be energy efficient as they are used in embedded devices in which battery life is important. In this study, we in- Body bias (BB) control is another solution that can im- vestigated the RTS energy efficiency by analyzing the ability of body bias prove RTS energy efficiency as it can manage the tradeoff (BB) in providing a satisfying tradeoff between performance and energy. between power leakage and performance without affecting We propose a practical and realistic model that includes the BB energy and the power supply [4], [5].Itseffect is further endorsed when timing overhead in addition to idle region analysis. This study was con- ducted using accurate parameters extracted from a real chip using silicon systems are enabled with silicon on thin box (SOTB) tech- on thin box (SOTB) technology. By using the BB control based on the nology [6], which is a novel and advanced fully depleted sili- proposed model, about 34% energy reduction was achieved.
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												  Hardware Mechanisms for Distributed Dynamic Software AnalysisHardware Mechanisms for Distributed Dynamic Software Analysis by Joseph Lee Greathouse A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Science and Engineering) in The University of Michigan 2012 Doctoral Committee: Professor Todd M. Austin, Chair Professor Scott Mahlke Associate Professor Robert Dick Associate Professor Jason Nelson Flinn c Joseph Lee Greathouse All Rights Reserved 2012 To my parents, Gail and Russell Greathouse. Without their support throughout my life, I would never have made it this far. ii Acknowledgments First and foremost, I must thank my advisor, Professor Todd Austin, for his help and guid- ance throughout my graduate career. I started graduate school with the broad desire to “research computer architecture,” but under Professor Austin’s watch, I have been able to hone this into work that interests us both and has real applications. His spot-on advice about choosing topics, performing research, writing papers, and giving talks has been an invaluable introduction to the research world. The members of my committee, Professors Robert Dick, Jason Flinn, and Scott Mahlke, also deserve special gratitude. Their insights, comments, and suggestions have immea- surably improved this dissertation. Together their expertise spans low-level hardware to systems software and beyond. This meant that I needed to ensure that any of my ideas were defensible from all sides. I have been fortunate enough to collaborate with numerous co-authors. I have often published with Professor Valeria Bertacco, who, much like Professor Austin, has given me invaluable advice during my time at Michigan. I am extremely lucky to have had the chance to work closely with Ilya Wagner, David Ramos, and Andrea Pellegrini, all of whom have continued to be good friends even after the high-stress publication process.
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												  ESC-470: ARM 9 Instruction Set Architecture with PerformanceARM 9 Instruction Set Architecture Introduction with Performance Perspective Joe-Ming Cheng, Ph.D. ARM-family processors are positioned among the leaders in key embedded applications. Many presentations and short lectures have already addressed the ARM’s applications and capabilities. In this introduction, we intend to discuss the ARM’s instruction set uniqueness from the performance prospective. This introduction is also trying to follow the approaches established by two outstanding textbooks of David Patterson and John Hennessey [PetHen00] [HenPet02]. 1.0 ARM Instruction Set Architecture Processor instruction set architecture (ISA) choices have evolved from accumulator, stack, register-to- memory, to register-register (load-store) organization. ARM 9 ISA is a load-store machine. ARM 9 ISA takes advantage of its smaller set of registers (16 vs. many 32-register processors) to incorporate more direct controls and achieve high encoding density. ARM’s load or store multiple register instruction, for example , allows enlisting of all possible registers and conditional execution in one instruction. The Thumb mode instruction set is another exa mple of how ARM ISA facilitates higher encode density. Rather than compressing the code, Thumb -mode instructions are two 16-bit instructions packed in a 32-bit ARM-mode instruction space. The Thumb -mode instructions are a subset of ARM instructions. When executing in Thumb mode, a single 32-bit instruction fetch cycle effectively brings in two instructions. Thumb code reduces access bandwidth, code size, and improves instruction cache hit rate. Another way ARM achieves cycle time reduction is by using Harvard architecture. The architecture facilitates independent data and instruction buses.
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												  RISC-V GeneologyRISC-V Geneology Tony Chen David A. Patterson Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2016-6 http://www.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-6.html January 24, 2016 Copyright © 2016, by the author(s). All rights reserved. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. Introduction RISC-V is an open instruction set designed along RISC principles developed originally at UC Berkeley1 and is now set to become an open industry standard under the governance of the RISC-V Foundation (www.riscv.org). Since the instruction set architecture (ISA) is unrestricted, organizations can share implementations as well as open source compilers and operating systems. Designed for use in custom systems on a chip, RISC-V consists of a base set of instructions called RV32I along with optional extensions for multiply and divide (RV32M), atomic operations (RV32A), single-precision floating point (RV32F), and double-precision floating point (RV32D). The base and these four extensions are collectively called RV32G. This report discusses the historical precedents of RV32G. We look at 18 prior instruction set architectures, chosen primarily from earlier UC Berkeley RISC architectures and major proprietary RISC instruction sets. Among the 122 instructions in RV32G: ● 6 instructions do not have precedents among the selected instruction sets, ● 98 instructions of the 116 with precedents appear in at least three different instruction sets.