# What is the difference between floating point and fixed-point representation?

What is the difference between floating point and fixed-point representation? A fixed point number just means that there are a fixed number of digits after the decimal point. A floating point number allows for a

## What is the difference between floating point and fixed-point representation?

A fixed point number just means that there are a fixed number of digits after the decimal point. A floating point number allows for a varying number of digits after the decimal point. For example, if you have a way of storing numbers that requires exactly four digits after the decimal point, then it is fixed point.

### What is meant by fixed-point representation?

In computing, fixed-point refers to a method of representing fractional (non-integer) numbers by storing a fixed number of digits of their fractional part. Dollar amounts, for example, are often stored with exactly two fractional digits, representing the cents (1/100 of dollar).

Is fixed point faster than floating-point?

Fixed point math, independent of processor speed, is easier to code with and faster than floating point math. Fixed point is adequate unless you know that you will be dealing with higher numbers than the fixed-point unit can handle. Floating point numbers also fit into a specific pattern.

What is the advantage of normalized floating point number?

A normalized number provides more accuracy than corresponding de-normalized number. The implied most significant bit can be used to represent even more accurate significand (23 + 1 = 24 bits) which is called subnormal representation. The floating point numbers are to be represented in normalized form.

## What are the advantages of floating-point over fixed point representation?

As such, floating point can support a much wider range of values than fixed point, with the ability to represent very small numbers and very large numbers.

### Is fixed point arithmetic faster?

Fixed point generally doesn’t provide much of an advantage in speed though, because of its limited representation range: how many bits would you need to represent 1.7E+/-308 with 15 digits of precision, the same as a 64-bit double? If my calculations are correct, you’d need somewhere around 2020 bits.