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

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Presentation on theme: "1 William Stallings Computer Organization and Architecture 6 th Edition Chapter 9 Computer Arithmetic."— Presentation transcript:

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

2 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: =

3 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

4 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 = =

5 5 5 Two’s Compliment +3 = = = = = = =

6 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

7 7 7 Geometric Depiction of Twos Complement Integers

8 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

9 9 9 Range of Numbers

10 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)

11 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

12 12 Hardware for Addition and Subtraction

13 13

14 14

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

16 16 Unsigned Binary Multiplication

17 17 Flowchart for Unsigned Binary Multiplication

18 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

19 19

20 20

21 21 Booth’s Algorithm

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

23 23 Division of Unsigned Binary Integers

24 24 Flowchart for Unsigned Binary Division

25 25

26 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?

27 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

28 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).

29 29 Floating Point Examples

30 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

31 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

32 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

33 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

34 34 Expressible Numbers

35 35 Density of Floating Point Numbers

36 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

37 37

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

39 39 FP Addition & Subtraction Flowchart

40 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

41 41 Floating Point Multiplication

42 42 Floating Point Division


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