In-System FPGA Prototyping of an Itanium Microarchitecture
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1 Introduction
Cambridge University Press 978-0-521-76992-1 - Microprocessor Architecture: From Simple Pipelines to Chip Multiprocessors Jean-Loup Baer Excerpt More information 1 Introduction Modern computer systems built from the most sophisticated microprocessors and extensive memory hierarchies achieve their high performance through a combina- tion of dramatic improvements in technology and advances in computer architec- ture. Advances in technology have resulted in exponential growth rates in raw speed (i.e., clock frequency) and in the amount of logic (number of transistors) that can be put on a chip. Computer architects have exploited these factors in order to further enhance performance using architectural techniques, which are the main subject of this book. Microprocessors are over 30 years old: the Intel 4004 was introduced in 1971. The functionality of the 4004 compared to that of the mainframes of that period (for example, the IBM System/370) was minuscule. Today, just over thirty years later, workstations powered by engines such as (in alphabetical order and without specific processor numbers) the AMD Athlon, IBM PowerPC, Intel Pentium, and Sun UltraSPARC can rival or surpass in both performance and functionality the few remaining mainframes and at a much lower cost. Servers and supercomputers are more often than not made up of collections of microprocessor systems. It would be wrong to assume, though, that the three tenets that computer archi- tects have followed, namely pipelining, parallelism, and the principle of locality, were discovered with the birth of microprocessors. They were all at the basis of the design of previous (super)computers. The advances in technology made their implementa- tions more practical and spurred further refinements. -
On the Hardware Reduction of Z-Datapath of Vectoring CORDIC
On the Hardware Reduction of z-Datapath of Vectoring CORDIC R. Stapenhurst*, K. Maharatna**, J. Mathew*, J.L.Nunez-Yanez* and D. K. Pradhan* *University of Bristol, Bristol, UK **University of Southampton, Southampton, UK [email protected] Abstract— In this article we present a novel design of a hardware wordlength larger than 18-bits the hardware requirement of it optimal vectoring CORDIC processor. We present a mathematical becomes more than the classical CORDIC. theory to show that using bipolar binary notation it is possible to eliminate all the arithmetic computations required along the z- In this particular work we propose a formulation to eliminate datapath. Using this technique it is possible to achieve three and 1.5 all the arithmetic operations along the z-datapath for conventional times reduction in the number of registers and adder respectively two-sided vector rotation and thereby reducing the hardware compared to classical CORDIC. Following this, a 16-bit vectoring while increasing the accuracy. Also the resulting architecture CORDIC is designed for the application in Synchronizer for IEEE shows significant hardware saving as the wordlength increases. 802.11a standard. The total area and dynamic power consumption Although we stick to the 2’s complement number system, without of the processor is 0.14 mm2 and 700μW respectively when loss of generality, this formulation can be adopted easily for synthesized in 0.18μm CMOS library which shows its effectiveness redundant arithmetic and higher radix formulation. A 16-bit as a low-area low-power processor. processor developed following this formulation requires 0.14 mm2 area and consumes 700 μW dynamic power when synthesized in 0.18μm CMOS library. -
Instruction Latencies and Throughput for AMD and Intel X86 Processors
Instruction latencies and throughput for AMD and Intel x86 processors Torbj¨ornGranlund 2019-08-02 09:05Z Copyright Torbj¨ornGranlund 2005{2019. Verbatim copying and distribution of this entire article is permitted in any medium, provided this notice is preserved. This report is work-in-progress. A newer version might be available here: https://gmplib.org/~tege/x86-timing.pdf In this short report we present latency and throughput data for various x86 processors. We only present data on integer operations. The data on integer MMX and SSE2 instructions is currently limited. We might present more complete data in the future, if there is enough interest. There are several reasons for presenting this report: 1. Intel's published data were in the past incomplete and full of errors. 2. Intel did not publish any data for 64-bit operations. 3. To allow straightforward comparison of an important aspect of AMD and Intel pipelines. The here presented data is the result of extensive timing tests. While we have made an effort to make sure the data is accurate, the reader is cautioned that some errors might have crept in. 1 Nomenclature and notation LNN means latency for NN-bit operation.TNN means throughput for NN-bit operation. The term throughput is used to mean number of instructions per cycle of this type that can be sustained. That implies that more throughput is better, which is consistent with how most people understand the term. Intel use that same term in the exact opposite meaning in their manuals. The notation "P6 0-E", "P4 F0", etc, are used to save table header space. -
18-447 Computer Architecture Lecture 6: Multi-Cycle and Microprogrammed Microarchitectures
18-447 Computer Architecture Lecture 6: Multi-Cycle and Microprogrammed Microarchitectures Prof. Onur Mutlu Carnegie Mellon University Spring 2015, 1/28/2015 Agenda for Today & Next Few Lectures n Single-cycle Microarchitectures n Multi-cycle and Microprogrammed Microarchitectures n Pipelining n Issues in Pipelining: Control & Data Dependence Handling, State Maintenance and Recovery, … n Out-of-Order Execution n Issues in OoO Execution: Load-Store Handling, … 2 Reminder on Assignments n Lab 2 due next Friday (Feb 6) q Start early! n HW 1 due today n HW 2 out n Remember that all is for your benefit q Homeworks, especially so q All assignments can take time, but the goal is for you to learn very well 3 Lab 1 Grades 25 20 15 10 5 Number of Students 0 30 40 50 60 70 80 90 100 n Mean: 88.0 n Median: 96.0 n Standard Deviation: 16.9 4 Extra Credit for Lab Assignment 2 n Complete your normal (single-cycle) implementation first, and get it checked off in lab. n Then, implement the MIPS core using a microcoded approach similar to what we will discuss in class. n We are not specifying any particular details of the microcode format or the microarchitecture; you can be creative. n For the extra credit, the microcoded implementation should execute the same programs that your ordinary implementation does, and you should demo it by the normal lab deadline. n You will get maximum 4% of course grade n Document what you have done and demonstrate well 5 Readings for Today n P&P, Revised Appendix C q Microarchitecture of the LC-3b q Appendix A (LC-3b ISA) will be useful in following this n P&H, Appendix D q Mapping Control to Hardware n Optional q Maurice Wilkes, “The Best Way to Design an Automatic Calculating Machine,” Manchester Univ. -
Datapath Design I Systems I
Systems I Datapath Design I Topics Sequential instruction execution cycle Instruction mapping to hardware Instruction decoding Overview How do we build a digital computer? Hardware building blocks: digital logic primitives Instruction set architecture: what HW must implement Principled approach Hardware designed to implement one instruction at a time Plus connect to next instruction Decompose each instruction into a series of steps Expect that most steps will be common to many instructions Extend design from there Overlap execution of multiple instructions (pipelining) Later in this course Parallel execution of many instructions In more advanced computer architecture course 2 Y86 Instruction Set Byte 0 1 2 3 4 5 nop 0 0 addl 6 0 halt 1 0 subl 6 1 rrmovl rA, rB 2 0 rA rB andl 6 2 irmovl V, rB 3 0 8 rB V xorl 6 3 rmmovl rA, D(rB) 4 0 rA rB D jmp 7 0 mrmovl D(rB), rA 5 0 rA rB D jle 7 1 OPl rA, rB 6 fn rA rB jl 7 2 jXX Dest 7 fn Dest je 7 3 call Dest 8 0 Dest jne 7 4 ret 9 0 jge 7 5 pushl rA A 0 rA 8 jg 7 6 popl rA B 0 rA 8 3 Building Blocks fun Combinational Logic A A = Compute Boolean functions of L U inputs B 0 Continuously respond to input changes MUX Operate on data and implement 1 control Storage Elements valA A srcA Store bits valW Register W file dstW Addressable memories valB B Non-addressable registers srcB Clock Loaded only as clock rises Clock 4 Hardware Control Language Very simple hardware description language Can only express limited aspects of hardware operation Parts we want to explore and modify Data -
A Highly Configurable High-Level Synthesis Functional Pattern Library
electronics Article A Highly Configurable High-Level Synthesis Functional Pattern Library Lan Huang 1,2,‡, Teng Gao 1,‡, Dalin Li 1,†, Zihao Wang 1 and Kangping Wang 1,2,* 1 College of Computer Science and Technology, Jilin University, Changchun 130012, China; [email protected] (L.H.); [email protected] (T.G.); [email protected] (D.L.); [email protected] (Z.W.) 2 Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Changchun 130012, China * Correspondence: [email protected] † Current address: Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Department of Computer Science and Technology, Zhuhai College of Jilin University, Zhuhai 519041, China. ‡ These authors contributed equally to this work. Abstract: FPGA has recently played an increasingly important role in heterogeneous computing, but Register Transfer Level design flows are not only inefficient in design, but also require designers to be familiar with the circuit architecture. High-level synthesis (HLS) allows developers to design FPGA circuits more efficiently with a more familiar programming language, a higher level of abstraction, and automatic adaptation of timing constraints. When using HLS tools, such as Xilinx Vivado HLS, specific design patterns and techniques are required in order to create high-performance circuits. Moreover, designing efficient concurrency and data flow structures requires a deep understanding of the hardware, imposing more learning costs on programmers. In this paper, we propose a set of functional patterns libraries based on the MapReduce model, implemented by C++ templates, Citation: Huang, L.; Gao,T.; Li, D.; which can quickly implement high-performance parallel pipelined computing models on FPGA with Wang, Z.; Wang, K. -
Richard S. Uhler
Smten and the Art of Satisfiability-Based Search by Richard S. Uhler B.S., University of California, Los Angeles (2008) S.M., Massachusetts Institute of Technology (2010) Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical Engineering and Computer Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY September 2014 c Massachusetts Institute of Technology 2014. All rights reserved. Author.............................................................. Department of Electrical Engineering and Computer Science August 22, 2014 Certified by. Jack B. Dennis Professor of Computer Science and Engineering Emeritus Thesis Supervisor Accepted by . Leslie A. Kolodziejski Chairman, Committee for Graduate Students 2 Smten and the Art of Satisfiability-Based Search by Richard S. Uhler Submitted to the Department of Electrical Engineering and Computer Science on August 22, 2014, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical Engineering and Computer Science Abstract Satisfiability (SAT) and Satisfiability Modulo Theories (SMT) have been leveraged in solving a wide variety of important and challenging combinatorial search prob- lems, including automatic test generation, logic synthesis, model checking, program synthesis, and software verification. Though in principle SAT and SMT solvers sim- plify the task of developing practical solutions to these hard combinatorial search problems, in practice developing an application that effectively uses a SAT or SMT solver can be a daunting task. SAT and SMT solvers have limited support, if any, for queries described using the wide array of features developers rely on for describing their programs: procedure calls, loops, recursion, user-defined data types, recursively defined data types, and other mechanisms for abstraction, encapsulation, and modu- larity. -
FPGA Programming for the Masses
practice DOI:10.1145/2436256.2436271 used to implement sequential logic. Article development led by queue.acm.org Apart from the homogeneous array of logic cells, an FPGA also contains discrete components such as BRAMs The programmability of FPGAs must improve (block RAMs), digital signal process- if they are to be part of mainstream computing. ing (DSP) slices, processor cores, and various communication cores (for ex- BY DavID F. BACON, RODRIC RabbaH, AND SUNIL SHUkla ample, Ethernet MAC and PCIe). BRAMs, which are specialized mem- ory structures distributed throughout the FPGA fabric in columns, are of par- ticular importance. Each BRAM can FPGA hold up to 36Kbits of data. BRAMs can be used in various form factors and can be cascaded to form a larger logical memory structure. Because of the dis- Programming tributed organization of BRAMs, they can provide terabytes of bandwidth for memory bandwidth-intensive applica- tions. Xilinx and Altera dominate the PLD market, collectively holding more for the Masses than an 85% share. FPGAs were long considered low- volume, low-density ASIC replace- ments. Following Moore’s Law, how- ever, FPGAs are getting denser and faster. Modern-day FPGAs can have up to two million logic cells, 68Mbits WHEN LOOKING AT how hardware influences of BRAM, more than 3,000 DSP slices, computing performance, we have general-purpose and up to 96 transceivers for imple- menting multigigabit communication processors (GPPs) on one end of the spectrum and channels.23 The latest FPGA families application-specific integrated circuits (ASICs) on the from Xilinx and Altera are more like a system-on-chip (SoC), mixing dual- other. -
Exam 1 Solutions
Midterm Exam ECE 741 – Advanced Computer Architecture, Spring 2009 Instructor: Onur Mutlu TAs: Michael Papamichael, Theodoros Strigkos, Evangelos Vlachos February 25, 2009 EXAM 1 SOLUTIONS Problem Points Score 1 40 2 20 3 15 4 20 5 25 6 20 7 (bonus) 15 Total 140+15 • This is a closed book midterm. You are allowed to have only two letter-sized cheat sheets. • No electronic devices may be used. • This exam lasts 1 hour 50 minutes. • If you make a mess, clearly indicate your final answer. • For questions requiring brief answers, please provide brief answers. Do not write an essay. You can be penalized for verbosity. • Please show your work when needed. We cannot give you partial credit if you do not clearly show how you arrive at a numerical answer. • Please write your name on every sheet. EXAM 1 SOLUTIONS Problem 1 (Short answers – 40 points) i. (3 points) A cache has the block size equal to the word length. What property of program behavior, which usually contributes to higher performance if we use a cache, does not help the performance if we use THIS cache? Spatial locality ii. (3 points) Pipelining increases the performance of a processor if the pipeline can be kept full with useful instructions. Two reasons that often prevent the pipeline from staying full with useful instructions are (in two words each): Data dependencies Control dependencies iii. (3 points) The reference bit (sometimes called “access” bit) in a PTE (Page Table Entry) is used for what purpose? Page replacement The similar function is performed by what bit or bits in a cache’s tag store entry? Replacement policy bits (e.g. -
LECTURE 5 Single-Cycle Datapath and Control
Single-Cycle LECTURE 5 Datapath and Control PROCESSORS In lecture 1, we reminded ourselves that the datapath and control are the two components that come together to be collectively known as the processor. • Datapath consists of the functional units of the processor. • Elements that hold data. • Program counter, register file, instruction memory, etc. • Elements that operate on data. • ALU, adders, etc. • Buses for transferring data between elements. • Control commands the datapath regarding when and how to route and operate on data. MIPS To showcase the process of creating a datapath and designing a control, we will be using a subset of the MIPS instruction set. Our available instructions include: • add, sub, and, or, slt • lw, sw • beq, j DATAPATH To start, we will look at the datapath elements needed by every instruction. First, we have instruction memory. Instruction memory is a state element that provides read-access to the instructions of a program and, given an address as input, supplies the corresponding instruction at that address. Code can also be written, e.g., self-modifying code DATAPATH Next, we have the program counter or PC. The PC is a state element that holds the address of the current instruction. Essentially, it is just a 32-bit register which holds the instruction address and is updated at the end of every clock cycle. Normally PC increments sequentially except for branch instructions The arrows on either side indicate that the PC state element is both readable and writeable. DATAPATH Lastly, we have the adder. The adder is responsible for incrementing the PC to hold the address of the next instruction. -
Trends in Processor Architecture
A. González Trends in Processor Architecture Trends in Processor Architecture Antonio González Universitat Politècnica de Catalunya, Barcelona, Spain 1. Past Trends Processors have undergone a tremendous evolution throughout their history. A key milestone in this evolution was the introduction of the microprocessor, term that refers to a processor that is implemented in a single chip. The first microprocessor was introduced by Intel under the name of Intel 4004 in 1971. It contained about 2,300 transistors, was clocked at 740 KHz and delivered 92,000 instructions per second while dissipating around 0.5 watts. Since then, practically every year we have witnessed the launch of a new microprocessor, delivering significant performance improvements over previous ones. Some studies have estimated this growth to be exponential, in the order of about 50% per year, which results in a cumulative growth of over three orders of magnitude in a time span of two decades [12]. These improvements have been fueled by advances in the manufacturing process and innovations in processor architecture. According to several studies [4][6], both aspects contributed in a similar amount to the global gains. The manufacturing process technology has tried to follow the scaling recipe laid down by Robert N. Dennard in the early 1970s [7]. The basics of this technology scaling consists of reducing transistor dimensions by a factor of 30% every generation (typically 2 years) while keeping electric fields constant. The 30% scaling in the dimensions results in doubling the transistor density (doubling transistor density every two years was predicted in 1975 by Gordon Moore and is normally referred to as Moore’s Law [21][22]). -
Design Automation for Streaming Systems
Design Automation for Streaming Systems Eylon Caspi Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2005-27 http://www.eecs.berkeley.edu/Pubs/TechRpts/2005/EECS-2005-27.html December 16, 2005 Copyright © 2005, 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. Design Automation for Streaming Systems by Eylon Caspi B.S. (University of Maryland, College Park) 1996 M.S. (University of California, Berkeley) 2000 A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the GRADUATE DIVISION of the UNIVERSITY OF CALIFORNIA, BERKELEY Committee in charge: Professor John Wawrzynek, Chair Professor Edward A. Lee Professor Robert Jacobsen Fall 2005 Design Automation for Streaming Systems Copyright c 2005 by Eylon Caspi Abstract Design Automation for Streaming Systems by Eylon Caspi Doctor of Philosophy in Computer Science University of California, Berkeley Professor John Wawrzynek, Chair RTL design methodologies are struggling to meet the challenges of modern, large system design. Their reliance on manually timed design with fully exposed device resources is laborious, restricts reuse, and is increasingly ineffective in an era of Moore’s Law expansion and growing interconnect delay.