CO212 Lecture 10: Arithmetic & Logical Unit

Size: px
Start display at page:

Download "CO212 Lecture 10: Arithmetic & Logical Unit"

Transcription

1 CO212 Lecture 10: Arithmetic & Logical Unit Shobhanjana Kalita, Dept. of CSE, Tezpur University Slides courtesy: Computer Architecture and Organization, 9 th Ed, W. Stallings

2 Integer Representation For purposes of computer storage and processing, however, we do not have the benefit of special symbols for the minus sign and radix point. Only binary digits (0 and 1) may be used to represent numbers. If we are limited to nonnegative integers 8-bit word can represent the numbers from 0 to 255 If we are to include negative numbers as well, there are different conventions such as Sign-Magnitude representation 1 s complement representation 2 s complement representation

3 Integer Representation Sign and Magnitude The simplest form of representation employs the MSB as a sign bit If the sign bit is 0, the number is positive; if the sign bit is 1, the number is negative. In an n-bit word, the rightmost (n 1) bits hold the magnitude of the integer. Example: Disadvantages: Addition and subtraction become complex Two representations of 0

4 Integer Representation 1 s complement 1 s complement representation also uses the MSB as a sign bit making it easy to test whether an integer is positive or negative Negative of a number is obtained by inverting all of the bits of the positive binary value Range of values: (2^(n 1) 1) to +(2^(n 1) 1) Example: +18 = = Addition and subtraction is simple addition of two binary numbers Disadvantage: Two representation for zero: is +0 and is 0

5 Integer Representation 2 s complement 2 s complement representation also uses the MSB as a sign bit making it easy to test whether an integer is positive or negative Negative of a number is obtained by inverting all of the bits of the positive binary value and adding a 1 to the result Range of values: (2^(n 1)) to +(2^(n 1) 1) Example: +18 = = Addition and subtraction is simple addition of two binary numbers One representation for zero: is 0

6 Integer Representation Most computer integer representations of computer uses 2 s complement Other signed integer representations may be used - Biased representation: add a bias value to the number and represent using the corresponding binary equivalent Fixed Point Representation Aforementioned representation are called fixed point representations because the radix point is assumed to have fixed position right of the rightmost digit (LSB)

7 Integer Arithmetic 2 s complement Negation Take the Boolean complement of each bit of the integer, add 1 to the result Negative of negative is positive Negative of -128??

8 Integer Arithmetic 2 s complement Addition and Subtraction Addition proceeds as simple addition of two unsigned integers In some instances, if there is a carry bit beyond the end of the word it is ignored. If the result is larger than can be held in the word size being used it is called overflow. When overflow occurs, the ALU must signal so that the result is not used for processing OVERFLOW RULE: If two numbers are added, and they are both positive or both negative, then overflow occurs if and only if the result has the opposite sign. Subtraction is achieved using addition. SUBTRACTION RULE: To subtract one number (subtrahend) from another (minuend), take the twos complement (negation) of the subtrahend and add it to the minuend.

9 Integer Arithmetic 2 s complement Multiplication Multiplication of positive numbers is straightforward 4-bit multiplied to 4-bit results in 8-bits For negative numbers, simple multiplication of 2 s complement value will not give the correct result (Eg. -5 * -3 = -113) Filling up the left most values in the partial product with binary 1s.

10 Integer Arithmetic 2 s complement Division Division of unsigned binary integers is straightforward 2 s complement division for negative numbers can be done by converting the operands into unsigned values perform division and then account for the sign on the resultant quotient D = Q * V + R sign(r) = sign(d) sign(q) = sign(d) * sign(v)

11 Floating Point Representation By assuming a fixed binary or radix point, numbers with a fractional component may be represented - but limited in the range of numbers Solution: Use scientific notation (floating point) 976,000,000,000,000 = 9.76 * 10^14, and = 9.76 * 10^(-14) Any number can be represented in the form Sign: + or ; Significand S; Exponent E The base B is implicit and is the same for all numbers

12 Floating Point Representation Sign 0 for positive; 1 for negative numbers Exponent Exponent is stored using a biased representation A fixed value, called the bias, is subtracted from the field to get the true exponent value Typically, bias is (2^(k 1) 1), where k is the number of bits in the binary exponent For an 8-bit field the range of unsigned numbers is 0 to 255 Bias = 2^7 1 = 127, actual exponent values are in the range -127 to +128 Advantage of biased representation: Comparison is easier

13 Floating Point Representation Significand A floating point number can have many representations To simplify operations need a standard representation normalization For a base 2 representation a normal number is one in which the MSB is of the significand is 1 i.e. In such normal numbers MSB is implicit, not stored 23bit field is used to store 24 bit significand The representation as presented will not accommodate a value of 0 include a special bit-pattern of 0

14 Floating Point Representation Overflow when an arithmetic operation results in an absolute value greater than can be expressed with an exponent of 128 Underflow when the fractional magnitude is too small less serious problem because result can generally be approximated by 0

15 Floating Point Representation Trade offs Numbers represented in floating-point notation are not spaced evenly along the number line, unlike fixed-point numbers Range determines maximum and minimum number of values that can be represented Precision determines the minimum value fractional value that can be represented accurately Trade-off between range and precision: more number of bits in significand => more precision more number of bits in exponent => greater range

16 Floating Point IEEE standard IEEE standard 754 for floating point numbers was developed to facilitate portability of programs from one processor to another 3 basic binary formats defined with lengths of 32, 64, and 128 bits Exponents of length 8, 11, 15 respectively The standard defines extended precision formats, which extend a supported basic format by providing additional bits in the exponent (extended range) and in the significand (extended precision) Defines only standards; implementation dependent

17 Floating Point IEEE standard

18 Floating Point Arithmetic For addition and subtraction both operands must have the same exponent value This may require shifting the radix point on one of the operands to achieve alignment Multiplication and division are more straightforward.

19 Floating Point Arithmetic A floating-point operation may produce one of these conditions: Exponent overflow: A positive exponent exceeds the maximum possible exponent value; may be designated as + or. Exponent underflow: A negative exponent is less than the minimum possible exponent value; it may be reported as 0. Significand underflow: While aligning significands, digits may flow off the right end of the significand; requires rounding Significand overflow: Addition of two significands of the same sign may result in a carry out of the most significant bit; requires realignment

20 Floating Point Arithmetic In floating-point arithmetic, addition and subtraction are more complex than multiplication and division need for alignment. There are four basic phases of the algorithm for addition and subtraction 1. Check for zeros 2. Align the significands 3. Add or subtract the significands 4. Normalize the result.

21 Floating Point Guard Bits For a floating-point operation, the exponent and significand of each operand are loaded into ALU registers In case of significand, the length of the register is greater than the length of the significand plus an implied bit The register contains additional bits, called guard bits, which are used to pad out the right end of the significand with 0s Important: to maintain precision Example: x = * 2^1 and y = * 2^0

22 Floating Point Rounding The result of any operation on the significands is generally stored in a longer register When the result is put back into the floating-point format, the extra bits must be eliminated in such a way as to produce a result that is close to the exact result rounding. Round to nearest: The result is rounded to the nearest representable number Round toward + : The result is rounded up toward plus infinity. Round toward : The result is rounded down toward negative infinity Round toward 0: The result is rounded toward zero.

23 Floating Point Special cases: Denormalized numbers When exponent is all 0 s, it is considered a special case and the floating point number thus represented is assumed to be a denormalized number i.e. the implicit integral part of the significand is assumed to be 0 (not 1) To represent 0: Exponent all 0 s, significand all 0 s To signal error conditions: overflow, underflow, division by 0 NaN (not a number) Exponent of all 0 s and non=zero significand

COMPUTER ARITHMETIC (Part 1)

COMPUTER ARITHMETIC (Part 1) Eastern Mediterranean University School of Computing and Technology ITEC255 Computer Organization & Architecture COMPUTER ARITHMETIC (Part 1) Introduction The two principal concerns for computer arithmetic

More information

Module 2: Computer Arithmetic

Module 2: Computer Arithmetic Module 2: Computer Arithmetic 1 B O O K : C O M P U T E R O R G A N I Z A T I O N A N D D E S I G N, 3 E D, D A V I D L. P A T T E R S O N A N D J O H N L. H A N N E S S Y, M O R G A N K A U F M A N N

More information

Floating-Point Data Representation and Manipulation 198:231 Introduction to Computer Organization Lecture 3

Floating-Point Data Representation and Manipulation 198:231 Introduction to Computer Organization Lecture 3 Floating-Point Data Representation and Manipulation 198:231 Introduction to Computer Organization Instructor: Nicole Hynes nicole.hynes@rutgers.edu 1 Fixed Point Numbers Fixed point number: integer part

More information

Divide: Paper & Pencil

Divide: Paper & Pencil Divide: Paper & Pencil 1001 Quotient Divisor 1000 1001010 Dividend -1000 10 101 1010 1000 10 Remainder See how big a number can be subtracted, creating quotient bit on each step Binary => 1 * divisor or

More information

COMP2611: Computer Organization. Data Representation

COMP2611: Computer Organization. Data Representation COMP2611: Computer Organization Comp2611 Fall 2015 2 1. Binary numbers and 2 s Complement Numbers 3 Bits: are the basis for binary number representation in digital computers What you will learn here: How

More information

Floating Point. The World is Not Just Integers. Programming languages support numbers with fraction

Floating Point. The World is Not Just Integers. Programming languages support numbers with fraction 1 Floating Point The World is Not Just Integers Programming languages support numbers with fraction Called floating-point numbers Examples: 3.14159265 (π) 2.71828 (e) 0.000000001 or 1.0 10 9 (seconds in

More information

Chapter 10 - Computer Arithmetic

Chapter 10 - Computer Arithmetic Chapter 10 - Computer Arithmetic Luis Tarrataca luis.tarrataca@gmail.com CEFET-RJ L. Tarrataca Chapter 10 - Computer Arithmetic 1 / 126 1 Motivation 2 Arithmetic and Logic Unit 3 Integer representation

More information

Numeric Encodings Prof. James L. Frankel Harvard University

Numeric Encodings Prof. James L. Frankel Harvard University Numeric Encodings Prof. James L. Frankel Harvard University Version of 10:19 PM 12-Sep-2017 Copyright 2017, 2016 James L. Frankel. All rights reserved. Representation of Positive & Negative Integral and

More information

Floating Point Arithmetic

Floating Point Arithmetic Floating Point Arithmetic CS 365 Floating-Point What can be represented in N bits? Unsigned 0 to 2 N 2s Complement -2 N-1 to 2 N-1-1 But, what about? very large numbers? 9,349,398,989,787,762,244,859,087,678

More information

Organisasi Sistem Komputer

Organisasi Sistem Komputer LOGO Organisasi Sistem Komputer OSK 8 Aritmatika Komputer 1 1 PT. Elektronika FT UNY Does the calculations Arithmetic & Logic Unit Everything else in the computer is there to service this unit Handles

More information

Floating-point representations

Floating-point representations Lecture 10 Floating-point representations Methods of representing real numbers (1) 1. Fixed-point number system limited range and/or limited precision results must be scaled 100101010 1111010 100101010.1111010

More information

Floating-point representations

Floating-point representations Lecture 10 Floating-point representations Methods of representing real numbers (1) 1. Fixed-point number system limited range and/or limited precision results must be scaled 100101010 1111010 100101010.1111010

More information

Signed Multiplication Multiply the positives Negate result if signs of operand are different

Signed Multiplication Multiply the positives Negate result if signs of operand are different Another Improvement Save on space: Put multiplier in product saves on speed: only single shift needed Figure: Improved hardware for multiplication Signed Multiplication Multiply the positives Negate result

More information

Chapter 5 : Computer Arithmetic

Chapter 5 : Computer Arithmetic Chapter 5 Computer Arithmetic Integer Representation: (Fixedpoint representation): An eight bit word can be represented the numbers from zero to 255 including = 1 = 1 11111111 = 255 In general if an nbit

More information

ECE232: Hardware Organization and Design

ECE232: Hardware Organization and Design ECE232: Hardware Organization and Design Lecture 11: Floating Point & Floating Point Addition Adapted from Computer Organization and Design, Patterson & Hennessy, UCB Last time: Single Precision Format

More information

Chapter 3: Arithmetic for Computers

Chapter 3: Arithmetic for Computers Chapter 3: Arithmetic for Computers Objectives Signed and Unsigned Numbers Addition and Subtraction Multiplication and Division Floating Point Computer Architecture CS 35101-002 2 The Binary Numbering

More information

The Sign consists of a single bit. If this bit is '1', then the number is negative. If this bit is '0', then the number is positive.

The Sign consists of a single bit. If this bit is '1', then the number is negative. If this bit is '0', then the number is positive. IEEE 754 Standard - Overview Frozen Content Modified by on 13-Sep-2017 Before discussing the actual WB_FPU - Wishbone Floating Point Unit peripheral in detail, it is worth spending some time to look at

More information

Floating-point Arithmetic. where you sum up the integer to the left of the decimal point and the fraction to the right.

Floating-point Arithmetic. where you sum up the integer to the left of the decimal point and the fraction to the right. Floating-point Arithmetic Reading: pp. 312-328 Floating-Point Representation Non-scientific floating point numbers: A non-integer can be represented as: 2 4 2 3 2 2 2 1 2 0.2-1 2-2 2-3 2-4 where you sum

More information

C NUMERIC FORMATS. Overview. IEEE Single-Precision Floating-point Data Format. Figure C-0. Table C-0. Listing C-0.

C NUMERIC FORMATS. Overview. IEEE Single-Precision Floating-point Data Format. Figure C-0. Table C-0. Listing C-0. C NUMERIC FORMATS Figure C-. Table C-. Listing C-. Overview The DSP supports the 32-bit single-precision floating-point data format defined in the IEEE Standard 754/854. In addition, the DSP supports an

More information

CS 101: Computer Programming and Utilization

CS 101: Computer Programming and Utilization CS 101: Computer Programming and Utilization Jul-Nov 2017 Umesh Bellur (cs101@cse.iitb.ac.in) Lecture 3: Number Representa.ons Representing Numbers Digital Circuits can store and manipulate 0 s and 1 s.

More information

Floating Point Numbers

Floating Point Numbers Floating Point Numbers Summer 8 Fractional numbers Fractional numbers fixed point Floating point numbers the IEEE 7 floating point standard Floating point operations Rounding modes CMPE Summer 8 Slides

More information

The ALU consists of combinational logic. Processes all data in the CPU. ALL von Neuman machines have an ALU loop.

The ALU consists of combinational logic. Processes all data in the CPU. ALL von Neuman machines have an ALU loop. CS 320 Ch 10 Computer Arithmetic The ALU consists of combinational logic. Processes all data in the CPU. ALL von Neuman machines have an ALU loop. Signed integers are typically represented in sign-magnitude

More information

Number Systems Standard positional representation of numbers: An unsigned number with whole and fraction portions is represented as:

Number Systems Standard positional representation of numbers: An unsigned number with whole and fraction portions is represented as: N Number Systems Standard positional representation of numbers: An unsigned number with whole and fraction portions is represented as: a n a a a The value of this number is given by: = a n Ka a a a a a

More information

Chapter Three. Arithmetic

Chapter Three. Arithmetic Chapter Three 1 Arithmetic Where we've been: Performance (seconds, cycles, instructions) Abstractions: Instruction Set Architecture Assembly Language and Machine Language What's up ahead: Implementing

More information

Number Systems and Computer Arithmetic

Number Systems and Computer Arithmetic Number Systems and Computer Arithmetic Counting to four billion two fingers at a time What do all those bits mean now? bits (011011011100010...01) instruction R-format I-format... integer data number text

More information

Computer Architecture Chapter 3. Fall 2005 Department of Computer Science Kent State University

Computer Architecture Chapter 3. Fall 2005 Department of Computer Science Kent State University Computer Architecture Chapter 3 Fall 2005 Department of Computer Science Kent State University Objectives Signed and Unsigned Numbers Addition and Subtraction Multiplication and Division Floating Point

More information

Lecture 5. Computer Arithmetic. Ch 9 [Stal10] Integer arithmetic Floating-point arithmetic

Lecture 5. Computer Arithmetic. Ch 9 [Stal10] Integer arithmetic Floating-point arithmetic Lecture 5 Computer Arithmetic Ch 9 [Stal10] Integer arithmetic Floating-point arithmetic ALU ALU = Arithmetic Logic Unit (aritmeettis-looginen yksikkö) Actually performs operations on data Integer and

More information

Floating Point. CSE 351 Autumn Instructor: Justin Hsia

Floating Point. CSE 351 Autumn Instructor: Justin Hsia Floating Point CSE 351 Autumn 2017 Instructor: Justin Hsia Teaching Assistants: Lucas Wotton Michael Zhang Parker DeWilde Ryan Wong Sam Gehman Sam Wolfson Savanna Yee Vinny Palaniappan Administrivia Lab

More information

Floating Point. CSE 351 Autumn Instructor: Justin Hsia

Floating Point. CSE 351 Autumn Instructor: Justin Hsia Floating Point CSE 351 Autumn 2017 Instructor: Justin Hsia Teaching Assistants: Lucas Wotton Michael Zhang Parker DeWilde Ryan Wong Sam Gehman Sam Wolfson Savanna Yee Vinny Palaniappan http://xkcd.com/571/

More information

Inf2C - Computer Systems Lecture 2 Data Representation

Inf2C - Computer Systems Lecture 2 Data Representation Inf2C - Computer Systems Lecture 2 Data Representation Boris Grot School of Informatics University of Edinburgh Last lecture Moore s law Types of computer systems Computer components Computer system stack

More information

Floating Point (with contributions from Dr. Bin Ren, William & Mary Computer Science)

Floating Point (with contributions from Dr. Bin Ren, William & Mary Computer Science) Floating Point (with contributions from Dr. Bin Ren, William & Mary Computer Science) Floating Point Background: Fractional binary numbers IEEE floating point standard: Definition Example and properties

More information

MIPS Integer ALU Requirements

MIPS Integer ALU Requirements MIPS Integer ALU Requirements Add, AddU, Sub, SubU, AddI, AddIU: 2 s complement adder/sub with overflow detection. And, Or, Andi, Ori, Xor, Xori, Nor: Logical AND, logical OR, XOR, nor. SLTI, SLTIU (set

More information

Floating Point Numbers. Lecture 9 CAP

Floating Point Numbers. Lecture 9 CAP Floating Point Numbers Lecture 9 CAP 3103 06-16-2014 Review of Numbers Computers are made to deal with numbers What can we represent in N bits? 2 N things, and no more! They could be Unsigned integers:

More information

EE260: Logic Design, Spring n Integer multiplication. n Booth s algorithm. n Integer division. n Restoring, non-restoring

EE260: Logic Design, Spring n Integer multiplication. n Booth s algorithm. n Integer division. n Restoring, non-restoring EE 260: Introduction to Digital Design Arithmetic II Yao Zheng Department of Electrical Engineering University of Hawaiʻi at Mānoa Overview n Integer multiplication n Booth s algorithm n Integer division

More information

FLOATING POINT NUMBERS

FLOATING POINT NUMBERS Exponential Notation FLOATING POINT NUMBERS Englander Ch. 5 The following are equivalent representations of 1,234 123,400.0 x 10-2 12,340.0 x 10-1 1,234.0 x 10 0 123.4 x 10 1 12.34 x 10 2 1.234 x 10 3

More information

Floating Point. CSE 351 Autumn Instructor: Justin Hsia

Floating Point. CSE 351 Autumn Instructor: Justin Hsia Floating Point CSE 351 Autumn 2016 Instructor: Justin Hsia Teaching Assistants: Chris Ma Hunter Zahn John Kaltenbach Kevin Bi Sachin Mehta Suraj Bhat Thomas Neuman Waylon Huang Xi Liu Yufang Sun http://xkcd.com/899/

More information

Number Systems CHAPTER Positional Number Systems

Number Systems CHAPTER Positional Number Systems CHAPTER 2 Number Systems Inside computers, information is encoded as patterns of bits because it is easy to construct electronic circuits that exhibit the two alternative states, 0 and 1. The meaning of

More information

3.5 Floating Point: Overview

3.5 Floating Point: Overview 3.5 Floating Point: Overview Floating point (FP) numbers Scientific notation Decimal scientific notation Binary scientific notation IEEE 754 FP Standard Floating point representation inside a computer

More information

Number Systems. Decimal numbers. Binary numbers. Chapter 1 <1> 8's column. 1000's column. 2's column. 4's column

Number Systems. Decimal numbers. Binary numbers. Chapter 1 <1> 8's column. 1000's column. 2's column. 4's column 1's column 10's column 100's column 1000's column 1's column 2's column 4's column 8's column Number Systems Decimal numbers 5374 10 = Binary numbers 1101 2 = Chapter 1 1's column 10's column 100's

More information

Binary Addition. Add the binary numbers and and show the equivalent decimal addition.

Binary Addition. Add the binary numbers and and show the equivalent decimal addition. Binary Addition The rules for binary addition are 0 + 0 = 0 Sum = 0, carry = 0 0 + 1 = 0 Sum = 1, carry = 0 1 + 0 = 0 Sum = 1, carry = 0 1 + 1 = 10 Sum = 0, carry = 1 When an input carry = 1 due to a previous

More information

Chapter 2 Data Representations

Chapter 2 Data Representations Computer Engineering Chapter 2 Data Representations Hiroaki Kobayashi 4/21/2008 4/21/2008 1 Agenda in Chapter 2 Translation between binary numbers and decimal numbers Data Representations for Integers

More information

CPE300: Digital System Architecture and Design

CPE300: Digital System Architecture and Design CPE300: Digital System Architecture and Design Fall 2011 MW 17:30-18:45 CBC C316 Arithmetic Unit 10122011 http://www.egr.unlv.edu/~b1morris/cpe300/ 2 Outline Recap Fixed Point Arithmetic Addition/Subtraction

More information

UNIVERSITY OF MASSACHUSETTS Dept. of Electrical & Computer Engineering. Digital Computer Arithmetic ECE 666

UNIVERSITY OF MASSACHUSETTS Dept. of Electrical & Computer Engineering. Digital Computer Arithmetic ECE 666 UNIVERSITY OF MASSACHUSETTS Dept. of Electrical & Computer Engineering Digital Computer Arithmetic ECE 666 Part 4-C Floating-Point Arithmetic - III Israel Koren ECE666/Koren Part.4c.1 Floating-Point Adders

More information

Representing and Manipulating Floating Points

Representing and Manipulating Floating Points Representing and Manipulating Floating Points Jin-Soo Kim (jinsookim@skku.edu) Computer Systems Laboratory Sungkyunkwan University http://csl.skku.edu The Problem How to represent fractional values with

More information

Computer Arithmetic Ch 8

Computer Arithmetic Ch 8 Computer Arithmetic Ch 8 ALU Integer Representation Integer Arithmetic Floating-Point Representation Floating-Point Arithmetic 1 Arithmetic Logical Unit (ALU) (2) (aritmeettis-looginen yksikkö) Does all

More information

Computer Arithmetic Ch 8

Computer Arithmetic Ch 8 Computer Arithmetic Ch 8 ALU Integer Representation Integer Arithmetic Floating-Point Representation Floating-Point Arithmetic 1 Arithmetic Logical Unit (ALU) (2) Does all work in CPU (aritmeettis-looginen

More information

Foundations of Computer Systems

Foundations of Computer Systems 18-600 Foundations of Computer Systems Lecture 4: Floating Point Required Reading Assignment: Chapter 2 of CS:APP (3 rd edition) by Randy Bryant & Dave O Hallaron Assignments for This Week: Lab 1 18-600

More information

Chapter 03: Computer Arithmetic. Lesson 09: Arithmetic using floating point numbers

Chapter 03: Computer Arithmetic. Lesson 09: Arithmetic using floating point numbers Chapter 03: Computer Arithmetic Lesson 09: Arithmetic using floating point numbers Objective To understand arithmetic operations in case of floating point numbers 2 Multiplication of Floating Point Numbers

More information

UNIVERSITY OF MASSACHUSETTS Dept. of Electrical & Computer Engineering. Digital Computer Arithmetic ECE 666

UNIVERSITY OF MASSACHUSETTS Dept. of Electrical & Computer Engineering. Digital Computer Arithmetic ECE 666 UNIVERSITY OF MASSACHUSETTS Dept. of Electrical & Computer Engineering Digital Computer Arithmetic ECE 666 Part 4-A Floating-Point Arithmetic Israel Koren ECE666/Koren Part.4a.1 Preliminaries - Representation

More information

Chapter 2. Data Representation in Computer Systems

Chapter 2. Data Representation in Computer Systems Chapter 2 Data Representation in Computer Systems Chapter 2 Objectives Understand the fundamentals of numerical data representation and manipulation in digital computers. Master the skill of converting

More information

Floating point. Today! IEEE Floating Point Standard! Rounding! Floating Point Operations! Mathematical properties. Next time. !

Floating point. Today! IEEE Floating Point Standard! Rounding! Floating Point Operations! Mathematical properties. Next time. ! Floating point Today! IEEE Floating Point Standard! Rounding! Floating Point Operations! Mathematical properties Next time! The machine model Chris Riesbeck, Fall 2011 Checkpoint IEEE Floating point Floating

More information

9/3/2015. Data Representation II. 2.4 Signed Integer Representation. 2.4 Signed Integer Representation

9/3/2015. Data Representation II. 2.4 Signed Integer Representation. 2.4 Signed Integer Representation Data Representation II CMSC 313 Sections 01, 02 The conversions we have so far presented have involved only unsigned numbers. To represent signed integers, computer systems allocate the high-order bit

More information

Data Representations & Arithmetic Operations

Data Representations & Arithmetic Operations Data Representations & Arithmetic Operations Hiroaki Kobayashi 7/13/2011 7/13/2011 Computer Science 1 Agenda Translation between binary numbers and decimal numbers Data Representations for Integers Negative

More information

Floating Point Puzzles. Lecture 3B Floating Point. IEEE Floating Point. Fractional Binary Numbers. Topics. IEEE Standard 754

Floating Point Puzzles. Lecture 3B Floating Point. IEEE Floating Point. Fractional Binary Numbers. Topics. IEEE Standard 754 Floating Point Puzzles Topics Lecture 3B Floating Point IEEE Floating Point Standard Rounding Floating Point Operations Mathematical properties For each of the following C expressions, either: Argue that

More information

CHAPTER 5: Representing Numerical Data

CHAPTER 5: Representing Numerical Data CHAPTER 5: Representing Numerical Data The Architecture of Computer Hardware and Systems Software & Networking: An Information Technology Approach 4th Edition, Irv Englander John Wiley and Sons 2010 PowerPoint

More information

Decimal & Binary Representation Systems. Decimal & Binary Representation Systems

Decimal & Binary Representation Systems. Decimal & Binary Representation Systems Decimal & Binary Representation Systems Decimal & binary are positional representation systems each position has a value: d*base i for example: 321 10 = 3*10 2 + 2*10 1 + 1*10 0 for example: 101000001

More information

Chapter 4 Section 2 Operations on Decimals

Chapter 4 Section 2 Operations on Decimals Chapter 4 Section 2 Operations on Decimals Addition and subtraction of decimals To add decimals, write the numbers so that the decimal points are on a vertical line. Add as you would with whole numbers.

More information

Representing and Manipulating Floating Points

Representing and Manipulating Floating Points Representing and Manipulating Floating Points Jinkyu Jeong (jinkyu@skku.edu) Computer Systems Laboratory Sungkyunkwan University http://csl.skku.edu SSE23: Introduction to Computer Systems, Spring 218,

More information

VTU NOTES QUESTION PAPERS NEWS RESULTS FORUMS Arithmetic (a) The four possible cases Carry (b) Truth table x y

VTU NOTES QUESTION PAPERS NEWS RESULTS FORUMS Arithmetic (a) The four possible cases Carry (b) Truth table x y Arithmetic A basic operation in all digital computers is the addition and subtraction of two numbers They are implemented, along with the basic logic functions such as AND,OR, NOT,EX- OR in the ALU subsystem

More information

CHW 261: Logic Design

CHW 261: Logic Design CHW 261: Logic Design Instructors: Prof. Hala Zayed Dr. Ahmed Shalaby http://www.bu.edu.eg/staff/halazayed14 http://bu.edu.eg/staff/ahmedshalaby14# Slide 1 Slide 2 Slide 3 Digital Fundamentals CHAPTER

More information

1. NUMBER SYSTEMS USED IN COMPUTING: THE BINARY NUMBER SYSTEM

1. NUMBER SYSTEMS USED IN COMPUTING: THE BINARY NUMBER SYSTEM 1. NUMBER SYSTEMS USED IN COMPUTING: THE BINARY NUMBER SYSTEM 1.1 Introduction Given that digital logic and memory devices are based on two electrical states (on and off), it is natural to use a number

More information

15213 Recitation 2: Floating Point

15213 Recitation 2: Floating Point 15213 Recitation 2: Floating Point 1 Introduction This handout will introduce and test your knowledge of the floating point representation of real numbers, as defined by the IEEE standard. This information

More information

Binary Addition & Subtraction. Unsigned and Sign & Magnitude numbers

Binary Addition & Subtraction. Unsigned and Sign & Magnitude numbers Binary Addition & Subtraction Unsigned and Sign & Magnitude numbers Addition and subtraction of unsigned or sign & magnitude binary numbers by hand proceeds exactly as with decimal numbers. (In fact this

More information

Number System. Introduction. Decimal Numbers

Number System. Introduction. Decimal Numbers Number System Introduction Number systems provide the basis for all operations in information processing systems. In a number system the information is divided into a group of symbols; for example, 26

More information

Computer Architecture and IC Design Lab. Chapter 3 Part 2 Arithmetic for Computers Floating Point

Computer Architecture and IC Design Lab. Chapter 3 Part 2 Arithmetic for Computers Floating Point Chapter 3 Part 2 Arithmetic for Computers Floating Point Floating Point Representation for non integral numbers Including very small and very large numbers 4,600,000,000 or 4.6 x 10 9 0.0000000000000000000000000166

More information

CPE 323 REVIEW DATA TYPES AND NUMBER REPRESENTATIONS IN MODERN COMPUTERS

CPE 323 REVIEW DATA TYPES AND NUMBER REPRESENTATIONS IN MODERN COMPUTERS CPE 323 REVIEW DATA TYPES AND NUMBER REPRESENTATIONS IN MODERN COMPUTERS Aleksandar Milenković The LaCASA Laboratory, ECE Department, The University of Alabama in Huntsville Email: milenka@uah.edu Web:

More information

CPE 323 REVIEW DATA TYPES AND NUMBER REPRESENTATIONS IN MODERN COMPUTERS

CPE 323 REVIEW DATA TYPES AND NUMBER REPRESENTATIONS IN MODERN COMPUTERS CPE 323 REVIEW DATA TYPES AND NUMBER REPRESENTATIONS IN MODERN COMPUTERS Aleksandar Milenković The LaCASA Laboratory, ECE Department, The University of Alabama in Huntsville Email: milenka@uah.edu Web:

More information

EE 109 Unit 19. IEEE 754 Floating Point Representation Floating Point Arithmetic

EE 109 Unit 19. IEEE 754 Floating Point Representation Floating Point Arithmetic 1 EE 109 Unit 19 IEEE 754 Floating Point Representation Floating Point Arithmetic 2 Floating Point Used to represent very small numbers (fractions) and very large numbers Avogadro s Number: +6.0247 * 10

More information

Floating Point Numbers

Floating Point Numbers Floating Point Floating Point Numbers Mathematical background: tional binary numbers Representation on computers: IEEE floating point standard Rounding, addition, multiplication Kai Shen 1 2 Fractional

More information

MACHINE LEVEL REPRESENTATION OF DATA

MACHINE LEVEL REPRESENTATION OF DATA MACHINE LEVEL REPRESENTATION OF DATA CHAPTER 2 1 Objectives Understand how integers and fractional numbers are represented in binary Explore the relationship between decimal number system and number systems

More information

Chapter 2 Float Point Arithmetic. Real Numbers in Decimal Notation. Real Numbers in Decimal Notation

Chapter 2 Float Point Arithmetic. Real Numbers in Decimal Notation. Real Numbers in Decimal Notation Chapter 2 Float Point Arithmetic Topics IEEE Floating Point Standard Fractional Binary Numbers Rounding Floating Point Operations Mathematical properties Real Numbers in Decimal Notation Representation

More information

COMPUTER ORGANIZATION AND ARCHITECTURE

COMPUTER ORGANIZATION AND ARCHITECTURE COMPUTER ORGANIZATION AND ARCHITECTURE For COMPUTER SCIENCE COMPUTER ORGANIZATION. SYLLABUS AND ARCHITECTURE Machine instructions and addressing modes, ALU and data-path, CPU control design, Memory interface,

More information

10.1. Unit 10. Signed Representation Systems Binary Arithmetic

10.1. Unit 10. Signed Representation Systems Binary Arithmetic 0. Unit 0 Signed Representation Systems Binary Arithmetic 0.2 BINARY REPRESENTATION SYSTEMS REVIEW 0.3 Interpreting Binary Strings Given a string of s and 0 s, you need to know the representation system

More information

Internal Data Representation

Internal Data Representation Appendices This part consists of seven appendices, which provide a wealth of reference material. Appendix A primarily discusses the number systems and their internal representation. Appendix B gives information

More information

COSC 243. Data Representation 3. Lecture 3 - Data Representation 3 1. COSC 243 (Computer Architecture)

COSC 243. Data Representation 3. Lecture 3 - Data Representation 3 1. COSC 243 (Computer Architecture) COSC 243 Data Representation 3 Lecture 3 - Data Representation 3 1 Data Representation Test Material Lectures 1, 2, and 3 Tutorials 1b, 2a, and 2b During Tutorial a Next Week 12 th and 13 th March If you

More information

Floating Point January 24, 2008

Floating Point January 24, 2008 15-213 The course that gives CMU its Zip! Floating Point January 24, 2008 Topics IEEE Floating Point Standard Rounding Floating Point Operations Mathematical properties class04.ppt 15-213, S 08 Floating

More information

CHAPTER V NUMBER SYSTEMS AND ARITHMETIC

CHAPTER V NUMBER SYSTEMS AND ARITHMETIC CHAPTER V-1 CHAPTER V CHAPTER V NUMBER SYSTEMS AND ARITHMETIC CHAPTER V-2 NUMBER SYSTEMS RADIX-R REPRESENTATION Decimal number expansion 73625 10 = ( 7 10 4 ) + ( 3 10 3 ) + ( 6 10 2 ) + ( 2 10 1 ) +(

More information

Bryant and O Hallaron, Computer Systems: A Programmer s Perspective, Third Edition. Carnegie Mellon

Bryant and O Hallaron, Computer Systems: A Programmer s Perspective, Third Edition. Carnegie Mellon Carnegie Mellon Floating Point 15-213/18-213/14-513/15-513: Introduction to Computer Systems 4 th Lecture, Sept. 6, 2018 Today: Floating Point Background: Fractional binary numbers IEEE floating point

More information

carry in carry 1101 carry carry

carry in carry 1101 carry carry Chapter Binary arithmetic Arithmetic is the process of applying a mathematical operator (such as negation or addition) to one or more operands (the values being operated upon). Binary arithmetic works

More information

Floating Point. CSE 238/2038/2138: Systems Programming. Instructor: Fatma CORUT ERGİN. Slides adapted from Bryant & O Hallaron s slides

Floating Point. CSE 238/2038/2138: Systems Programming. Instructor: Fatma CORUT ERGİN. Slides adapted from Bryant & O Hallaron s slides Floating Point CSE 238/2038/2138: Systems Programming Instructor: Fatma CORUT ERGİN Slides adapted from Bryant & O Hallaron s slides Today: Floating Point Background: Fractional binary numbers IEEE floating

More information

CSCI 402: Computer Architectures. Arithmetic for Computers (3) Fengguang Song Department of Computer & Information Science IUPUI.

CSCI 402: Computer Architectures. Arithmetic for Computers (3) Fengguang Song Department of Computer & Information Science IUPUI. CSCI 402: Computer Architectures Arithmetic for Computers (3) Fengguang Song Department of Computer & Information Science IUPUI 3.5 Today s Contents Floating point numbers: 2.5, 10.1, 100.2, etc.. How

More information

Chapter 3: part 3 Binary Subtraction

Chapter 3: part 3 Binary Subtraction Chapter 3: part 3 Binary Subtraction Iterative combinational circuits Binary adders Half and full adders Ripple carry and carry lookahead adders Binary subtraction Binary adder-subtractors Signed binary

More information

Integers. N = sum (b i * 2 i ) where b i = 0 or 1. This is called unsigned binary representation. i = 31. i = 0

Integers. N = sum (b i * 2 i ) where b i = 0 or 1. This is called unsigned binary representation. i = 31. i = 0 Integers So far, we've seen how to convert numbers between bases. How do we represent particular kinds of data in a certain (32-bit) architecture? We will consider integers floating point characters What

More information

Representing and Manipulating Floating Points

Representing and Manipulating Floating Points Representing and Manipulating Floating Points Jin-Soo Kim (jinsookim@skku.edu) Computer Systems Laboratory Sungkyunkwan University http://csl.skku.edu The Problem How to represent fractional values with

More information

Representing and Manipulating Floating Points. Jo, Heeseung

Representing and Manipulating Floating Points. Jo, Heeseung Representing and Manipulating Floating Points Jo, Heeseung The Problem How to represent fractional values with finite number of bits? 0.1 0.612 3.14159265358979323846264338327950288... 2 Fractional Binary

More information

Floating Point Puzzles. Lecture 3B Floating Point. IEEE Floating Point. Fractional Binary Numbers. Topics. IEEE Standard 754

Floating Point Puzzles. Lecture 3B Floating Point. IEEE Floating Point. Fractional Binary Numbers. Topics. IEEE Standard 754 Floating Point Puzzles Topics Lecture 3B Floating Point IEEE Floating Point Standard Rounding Floating Point Operations Mathematical properties For each of the following C expressions, either: Argue that

More information

Chapter 4. Operations on Data

Chapter 4. Operations on Data Chapter 4 Operations on Data 1 OBJECTIVES After reading this chapter, the reader should be able to: List the three categories of operations performed on data. Perform unary and binary logic operations

More information

Floating point. Today. IEEE Floating Point Standard Rounding Floating Point Operations Mathematical properties Next time.

Floating point. Today. IEEE Floating Point Standard Rounding Floating Point Operations Mathematical properties Next time. Floating point Today IEEE Floating Point Standard Rounding Floating Point Operations Mathematical properties Next time The machine model Fabián E. Bustamante, Spring 2010 IEEE Floating point Floating point

More information

COMP Overview of Tutorial #2

COMP Overview of Tutorial #2 COMP 1402 Winter 2008 Tutorial #2 Overview of Tutorial #2 Number representation basics Binary conversions Octal conversions Hexadecimal conversions Signed numbers (signed magnitude, one s and two s complement,

More information

CS 33. Data Representation (Part 3) CS33 Intro to Computer Systems VIII 1 Copyright 2018 Thomas W. Doeppner. All rights reserved.

CS 33. Data Representation (Part 3) CS33 Intro to Computer Systems VIII 1 Copyright 2018 Thomas W. Doeppner. All rights reserved. CS 33 Data Representation (Part 3) CS33 Intro to Computer Systems VIII 1 Copyright 2018 Thomas W. Doeppner. All rights reserved. Byte-Oriented Memory Organization 00 0 FF F Programs refer to data by address

More information

UNIVERSITY OF MASSACHUSETTS Dept. of Electrical & Computer Engineering. Digital Computer Arithmetic ECE 666

UNIVERSITY OF MASSACHUSETTS Dept. of Electrical & Computer Engineering. Digital Computer Arithmetic ECE 666 UNIVERSITY OF MASSACHUSETTS Dept. of Electrical & Computer Engineering Digital Computer Arithmetic ECE 666 Part 4-B Floating-Point Arithmetic - II Israel Koren ECE666/Koren Part.4b.1 The IEEE Floating-Point

More information

Giving credit where credit is due

Giving credit where credit is due CSCE 230J Computer Organization Floating Point Dr. Steve Goddard goddard@cse.unl.edu http://cse.unl.edu/~goddard/courses/csce230j Giving credit where credit is due Most of slides for this lecture are based

More information

Computer (Literacy) Skills. Number representations and memory. Lubomír Bulej KDSS MFF UK

Computer (Literacy) Skills. Number representations and memory. Lubomír Bulej KDSS MFF UK Computer (Literacy Skills Number representations and memory Lubomír Bulej KDSS MFF UK Number representations? What for? Recall: computer works with binary numbers Groups of zeroes and ones 8 bits (byte,

More information

ecture 25 Floating Point Friedland and Weaver Computer Science 61C Spring 2017 March 17th, 2017

ecture 25 Floating Point Friedland and Weaver Computer Science 61C Spring 2017 March 17th, 2017 ecture 25 Computer Science 61C Spring 2017 March 17th, 2017 Floating Point 1 New-School Machine Structures (It s a bit more complicated!) Software Hardware Parallel Requests Assigned to computer e.g.,

More information

Giving credit where credit is due

Giving credit where credit is due JDEP 284H Foundations of Computer Systems Floating Point Dr. Steve Goddard goddard@cse.unl.edu Giving credit where credit is due Most of slides for this lecture are based on slides created by Drs. Bryant

More information

Rui Wang, Assistant professor Dept. of Information and Communication Tongji University.

Rui Wang, Assistant professor Dept. of Information and Communication Tongji University. Data Representation ti and Arithmetic for Computers Rui Wang, Assistant professor Dept. of Information and Communication Tongji University it Email: ruiwang@tongji.edu.cn Questions What do you know about

More information

System Programming CISC 360. Floating Point September 16, 2008

System Programming CISC 360. Floating Point September 16, 2008 System Programming CISC 360 Floating Point September 16, 2008 Topics IEEE Floating Point Standard Rounding Floating Point Operations Mathematical properties Powerpoint Lecture Notes for Computer Systems:

More information

Floating Point : Introduction to Computer Systems 4 th Lecture, May 25, Instructor: Brian Railing. Carnegie Mellon

Floating Point : Introduction to Computer Systems 4 th Lecture, May 25, Instructor: Brian Railing. Carnegie Mellon Floating Point 15-213: Introduction to Computer Systems 4 th Lecture, May 25, 2018 Instructor: Brian Railing Today: Floating Point Background: Fractional binary numbers IEEE floating point standard: Definition

More information

Representing and Manipulating Floating Points. Computer Systems Laboratory Sungkyunkwan University

Representing and Manipulating Floating Points. Computer Systems Laboratory Sungkyunkwan University Representing and Manipulating Floating Points Jin-Soo Kim (jinsookim@skku.edu) Computer Systems Laboratory Sungkyunkwan University http://csl.skku.edu The Problem How to represent fractional values with

More information

Systems I. Floating Point. Topics IEEE Floating Point Standard Rounding Floating Point Operations Mathematical properties

Systems I. Floating Point. Topics IEEE Floating Point Standard Rounding Floating Point Operations Mathematical properties Systems I Floating Point Topics IEEE Floating Point Standard Rounding Floating Point Operations Mathematical properties IEEE Floating Point IEEE Standard 754 Established in 1985 as uniform standard for

More information