IEEE754 floating-point representation

IEEE754 floating-point representation

Number representation

Floating point system sign bit exponent bit fraction bit(mantissa)
32-bits 1 8 23
64-bits 1 11 52

Following are representations of 1 in different floating point system. (s stands for sign, e for exponent, f for fraction)





Calculating floating point value

As we can image, the way to calculate the a floating point value is simply like:


where for sign bit: 0 = positive number, 1 = negative number

But there are a few things to notice:

First: In order to introduce small numbers like 0.0000123 into floating point system, we have to allow exponent to be negative. Therefore, the exponent part has bias, which equals to 2^{n-1}-1 where n is number of exponent bits in the floating point system, and the actual exponent representation equals to

    \[exponent - bias\]

where the bias in 32-bit system is 127, and that in 64-bit system is 1023.

Therefore, exponent 0 in 32-bit system is like:


Second: A nice little optimization is now available to us in base two, since binary has only one possible non-zero digit: 1. Thus, we can just assume a leading digit of 1, and don’t need to store it in the floating-point representation. As a result, we can assume a leading digit of 1 without storing it, so that a 32-bit floating-point value effectively has 24 bits of mantissa: 23 explicit fraction bits plus one implicit leading bit of 1.1

As the result, the real value of fraction part equals to:

    \[fraction*2 + 1\]

because we have an “invisible” 1 at the end of the mantissa.

Therefore, fraction 1 in 32-bit system is just an all-zero string.


So to conclude, we can generalize the following equation to calculate the real value of of a floating-point number:
1. for 32-bit float:


2. for 64-bit float:




Condition: When exponent part = 0 and fraction part = 0.

Since mantissa part always assumes an “1” at the end of fraction part, we defaults zero to be a number with exponent part = 0 and fraction part = 0.

Additionally, due to the existence of sign bit, there exists two zeros in floating number: +0 and -0, which are represented differently in bit level.


Condition: When exponent part = 2^n-1 and fraction part = 0.

Not A Number

Condition: When exponent part = 2^n-1 and fraction part is not 0

Special Operations

Operation Result
n\div\pm\infty 0
\pm\infty*\pm\infty \pm\infty
\pm x \div \pm0 \pm\infty
\pm x * \pm\infty \pm\infty
\infty + \infty \infty
-\infty -\infty -\infty
\infty - \infty NaN
-\infty + \infty NaN
\pm0 \div \pm0 NaN
\pm\infty \div \pm\infty NaN
\pm\infty * 0 NaN


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