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Dr. Konstantinos Tatas

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FLOATING-POINT NUMBER REPRESENTATION Dr. Konstantinos Tatas The range/accuracy problem Real numbers Instead of representing the actual value, in the base system, we ... – PowerPoint PPT presentation

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Title: Dr. Konstantinos Tatas


1
Dr. Konstantinos Tatas
  • FLOATING-POINT NUMBER REPRESENTATION

2
The range/accuracy problem
  • The range of numbers that can be represented with
    n bits is
  • In 2s complement from - /2 to /2 -1
  • For n8 From 128 to 127
  • For n16 From 32,768 to 32,767
  • Still, in many application an even larger range
    is required

3
Real numbers
  • Instead of representing the actual value, in the
    base system, we represent the sign, M, b and e

4
FLOATING-POINT REPRESENTATION
  • IEEE short real 8 bits for the exponent (in
    Ex-127), 23 bits for the mantissa
  • IEEE long real 11 bits for the exponent, 52 bits
    for the mantissa

Sign (S) Biased exponent (E) Unsigned normalized mantissa (M)
5
RESERVED VALUES
6
Examples (IEEE short real format)
Binary value Normalized Binary value exponent Biased Exponent (Excess -127 Sign, exponent, mantissa
-1.01 -1.01 0 127 1 01111111 0100000000000000000000
1011.0101 1.0110101 3 130 0 01000010 0110101000000000000000
-0.0000011 -1.1 -6 121 1 01111001 1000000000000000000000
11010101 1.11010101 7 134 0 10000110 1101010100000000000000
7
Homework
  • Convert the following 2s complement values to
    IEEE short real floating-point representation
  • 10011010
  • 0110.0101
  • 0.1111110
  • 1100.0001
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