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1 William Stallings Computer Organization and Architecture 6 th Edition Chapter 9 Computer Arithmetic

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2 2 Arithmetic & Logic Unit Does the calculations Everything else in the computer is there to service this unit Handles integers May handle floating point (real) numbers May be separate FPU (maths co-processor) May be on chip separate FPU (486DX +) Human: =

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3 3 Integer Representation Only have 0 & 1 to represent everything Positive numbers stored in binary e.g. 41= = No minus sign No period Sign-Magnitude Two’s compliment

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4 4 Sign-Magnitude Left most bit is sign bit 0 means positive 1 means negative +18 = = Problems Need to consider both sign and magnitude in arithmetic Two representations of zero (+0 and -0) +18 = =

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5 5 Two’s Compliment +3 = = = = = = =

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6 6 Benefits of Two’s Compliment One representation of zero Arithmetic works easily (see later) Negating is fairly easy 3 = Boolean complement gives Add 1 to LSB

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7 7 Geometric Depiction of Twos Complement Integers

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8 8 Range of Numbers 8 bit 2s compliment +127 = = = = bit 2s compliment = = = = Value range for an n-bit number Positive Number Range: 0 ~ 2 n-1 -1 Negative number range: -1 ~ - 2 n-1

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9 9 Range of Numbers

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10 Conversion Between Lengths Positive number pack with leading zeros +18 = = Negative numbers pack with leading ones -18 = = i.e. pack with MSB (sign bit)

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11 Addition and Subtraction Normal binary addition Monitor sign bit for overflow Take twos compliment of subtrahend and add to minuend i.e. a - b = a + (-b) So we only need addition and complement circuits

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12 Hardware for Addition and Subtraction

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15 Multiplication Complex Work out partial product for each digit Take care with place value (column) Add partial products Note: need double length result

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16 Unsigned Binary Multiplication

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17 Flowchart for Unsigned Binary Multiplication

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18 Multiplying Negative Numbers This does not work! Solution 1 Convert to positive if required Multiply as above If signs were different, negate answer Solution 2 Booth’s algorithm

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21 Booth’s Algorithm

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22 Division More complex than multiplication Negative numbers are really bad! Based on long division

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23 Division of Unsigned Binary Integers

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24 Flowchart for Unsigned Binary Division

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26 Real Numbers Numbers with fractions Could be done in pure binary = =9.625 Where is the binary point? Fixed? Very limited Moving? How do you show where it is?

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27 Scientific notation: Slide the decimal point to a convenient location Keep track of the decimal point use the exponent of 10 Do the same with binary number in the form of Sign: + or - Significant: S Exponent: E

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28 Floating Point +/-.significand x 2 exponent Point is actually fixed between sign bit and body of mantissa Exponent indicates place value (point position) 32-bit floating point format. Leftmost bit = sign bit (0 positive or 1 negative). Exponent in the next 8 bits. Use a biased representation. A fixed value, called bias, is subtracted from the field to get the true exponent value. Typically, bias = 2 k-1 - 1, where k is the number of bits in the exponent field. Also known as excess-N format, where N = bias = 2 k (The bias could take other values) In this case: 8-bit exponent field, Bias = 127. Exponent range -127 to +128 Final portion of word (23 bits in this example) is the significant (sometimes called mantissa).

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29 Floating Point Examples

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30 Signs for Floating Point Mantissa is stored in 2’s compliment Exponent is in excess or biased notation e.g. Excess (bias) 128 means 8 bit exponent field Pure value range Subtract 128 to get correct value Range -128 to +127

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31 Many ways to represent a floating point number, e.g., Normalization: Adjust the exponent such that the leading bit (MSB) of mantissa is always 1. In this example, a normalized nonzero number is in the form Left most bit always 1 - no need to store 23-bit field used to store 24-bit mantissa with a value between 1 to 2

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32 Normalization FP numbers are usually normalized i.e. exponent is adjusted so that leading bit (MSB) of mantissa is 1 Since it is always 1 there is no need to store it (c.f. Scientific notation where numbers are normalized to give a single digit before the decimal point) X X2 21

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33 FP Ranges For a 32 bit number 8 bit exponent +/ 1.5 x Accuracy The effect of changing LSB of mantissa 23 bit mantissa 1.2 x About 6 decimal places

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34 Expressible Numbers

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35 Density of Floating Point Numbers

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36 IEEE 754 Standard for floating point storage 32 and 64 bit standards 8 and 11 bit exponent respectively Extended formats (both mantissa and exponent) for intermediate results

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38 FP Arithmetic +/- Check for zeros Align significands (adjusting exponents) Add or subtract significands Normalize result

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39 FP Addition & Subtraction Flowchart

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40 FP Arithmetic x/ Check for zero Add/subtract exponents Multiply/divide significands (watch sign) Normalize Round All intermediate results should be in double length storage

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41 Floating Point Multiplication

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42 Floating Point Division

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