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Fixed Point Format and Floating Point Format Examples
• It is found that in the floatingpoint representation we can cover a much larger range of numbers than that is possible in the fixedpoint representation.
• In the floatingpoint representation the resolution decreases with an increase in the size of the range; this means that the distance between two successive floatingpoint numbers increases.
• In the floatingpoint scheme, resolution is variable within the range. However, for the fixedpoint format, resolution is fixed and uniform.
• This variability in resolution provides a large dynamic range of the numbers.
We have, for the smallest possible number in this scheme, mantissa = 0.5, which is the minimum possible value of mantissas in the floatingpoint scheme, as stated earlier. This is then represented under the column M as .1 followed by eight 0s (i.e., .100000000). The sign of the mantissa is taken as positive and is represented by a 0 under the column S_{M}. Since the exponent has 5 bits to represent it, the highest possible number in this case will be 2^{5} = 32. Using this as the exponent, we get the smallest number in our present case as
We find that this is achieved by sacrificing uniformity in resolution. Notice that in the floatingpoint format, compared to larger numbers, whose resolution is coarse, small numbers have finer resolution.
The IEEE 754 Standard (Floatingpoint format) for 32bit Machines
IEEE 754 standard for floatingpoint arithmetic in 32bit computers is shown in Table 1.39.
• In the floatingpoint representation the resolution decreases with an increase in the size of the range; this means that the distance between two successive floatingpoint numbers increases.
• In the floatingpoint scheme, resolution is variable within the range. However, for the fixedpoint format, resolution is fixed and uniform.
• This variability in resolution provides a large dynamic range of the numbers.
To the ideas given above can be illustrated, by considering case of a 16bit computer.
Example 35: Consider a 16bit computer. Obtain the dynamic range and resolution when the computer is operated (a) fixedpoint format and (b) in the floatingpoint format.
Solution:
(a) For the 16bit computer, with one bit reserved for representing the sign, the highest positive and negative numbers that can be represented in the fixedpoint format are (2^{m}^{}^{1}  1) and (2^{m}^{}^{1 } 1), respectively, where m = 16. Substituting for m = 16 yields:
The highest positive number = 2^{16}^{}^{1 } 1 = 2^{15}1 = 32767
The highest negative number = (2^{16}^{}^{1 } 1) = (2^{15}1) = 32767
This means that we can represent all the whole numbers from 32,767 to + 32,767. Since the numbers represented are of the form 32,767, 32,766, 32,765, …, 32,766, and 32,767 (i.e., successive numbers differing by one digit), we find that in this scheme:
Resolution = 1
In this scheme, we also find that we can represent only whole numbers; we can not represent fractions.
Now, suppose we want to express fractions also through this scheme. For this, let us reserve 5 bits to represent the fractional part, 10 bits to represent the integer part, and 1 bit to represent the sign of the mantissa. In the fixedpoint representation, the given number can now be written as
X = ± (2^{10}  1) ´ 2^{}^{5}^{ }= ± 31.96875
Thus the range in this case will be between 31.96875 and +31.96875. We have:
Resolution = 2^{}^{5}^{ }= 0.00001
We find that in this case, the range (sometimes called the dynamic range) has been considerably decreased, but resolution has been greatly increased.
(b) FloatingPoint Format
In the floatingpoint format, we reserve 5 bits to represent the exponent, 1 bit to represent its sign, 9 bits to represent the mantissa part, and 1 bit to represent its sign. Table 1.38 shows the floatingpoint representation of the given number.
Table 1.38 Floatingpoint representation in a 16bit computer
S_{M}

M (9 bits)

S_{E}

E (5 bits)

0

0.1 0 0 0 0 0 0 0 0

1

1 1 1 1 1

0

0.1 0 0 0 0 0 0 0 0

0

1 1 1 1 1

In Table 1.38, S_{M }_{ }represents the sign bit of mantissa, M represents the mantissa, S_{E} represents the sign bit of exponent, and E represents the exponent. We find that row 2 shows the smallest bit that can be represented in this format. This is obtained as follows:
We have, for the smallest possible number in this scheme, mantissa = 0.5, which is the minimum possible value of mantissas in the floatingpoint scheme, as stated earlier. This is then represented under the column M as .1 followed by eight 0s (i.e., .100000000). The sign of the mantissa is taken as positive and is represented by a 0 under the column S_{M}. Since the exponent has 5 bits to represent it, the highest possible number in this case will be 2^{5} = 32. Using this as the exponent, we get the smallest number in our present case as
We find that this is achieved by sacrificing uniformity in resolution. Notice that in the floatingpoint format, compared to larger numbers, whose resolution is coarse, small numbers have finer resolution.
The IEEE 754 Standard (Floatingpoint format) for 32bit Machines
IEEE 754 standard for floatingpoint arithmetic in 32bit computers is shown in Table 1.39.
Table 1.39 IEEE 754 standard for 32bit machines
Sign (S)

Exponent (E)

Mantissa (M)

0

1 8

9 31

In this scheme, we have 23 bits reserved for representing the mantissa, one bit for the sign of the mantissa (S), 7 bits for the exponent, and one bit for the sign of the exponent. The maximum number in this scheme is
This post was written by: Sreejith Hrishikeshan
Sreejith Hrishikesan Nair is a MTech graduate in Communication Systems. He completed Btech Degree in Electronics and Communication.He is a person who wants to implement new ideas in the field of Technology.
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