Multiplying Floating Point Numbers Last Updated : 12 Jul, 2025 Summarize Comments Improve Suggest changes Share Like Article Like Report Prerequisite - IEEE Standard 754 Floating Point Numbers Problem:- Here, we have discussed an algorithm to multiply two floating point numbers, x and y. Algorithm:- Convert these numbers in scientific notation, so that we can explicitly represent hidden 1. Let ‘a’ be the exponent of x and ‘b’ be the exponent of y. Assume resulting exponent c = a+b. It can be adjusted after the next step. Multiply mantissa of x to mantissa of y. Call this result m. If m does not have a single 1 left of radix point, then adjust radix point so it does, and adjust exponent c to compensate. Add sign bits, mod 2, to get sign of resulting multiplication. Convert back to one byte floating point representation, truncating bits if needed. Note : Negative values are simple to take care of in floating point multiplication. Treat sign bit as 1 bit unsigned binary, add mod 2. This is the same as XORing the sign bit. Example :- Suppose you want to multiply following two numbers: Now, these are steps according to above algorithm: Given, A = 1.11 x 2^0 and B = 1.01 x 2^2 So, exponent c = a + b = 0 + 2 = 2 is the resulting exponent. Now, multiply 1.11 by 1.01, so result will be 10.0011 We need to normalize 10.0011 to 1.00011 and adjust exponent 1 by 3 appropriately. Resulting sign bit 0 (XOR) 0 = 0, means positive. Now, truncate and normalite it 1.00011 x 2^3 to 1.000 x 2^3. Therefore, resultant number is, Similarly, we can multiply other floating point numbers. Comment More infoAdvertise with us Next Article Introduction of Floating Point Representation R rajkumarupadhyay515 Follow Improve Article Tags : Write From Home Digital Logic Similar Reads Introduction of Floating Point Representation The Floating point representation is a way to the encode numbers in a format that can handle very large and very small values. It is based on scientific notation where numbers are represented as a fraction and an exponent. In computing, this representation allows for trade-off between range and prec 5 min read Introduction of Floating Point Representation The Floating point representation is a way to the encode numbers in a format that can handle very large and very small values. It is based on scientific notation where numbers are represented as a fraction and an exponent. In computing, this representation allows for trade-off between range and prec 5 min read Floating Point Representation - Basics Floating-point representation helps computers handle real numbers with a large range of values, both very small and very large. The single precision format uses 32 bits, while double precision uses 64 bits, allowing for more precision and a larger range. These formats are based on scientific notatio 9 min read Decimal Number System A number system is a method for representing numbers, defined by its base or radix. The decimal number system, also known as base-10, is the number system we use every day for tasks like counting people, tracking scores, or tallying votes. It uses ten digits: 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9.In this 6 min read Decimal Number System A number system is a method for representing numbers, defined by its base or radix. The decimal number system, also known as base-10, is the number system we use every day for tasks like counting people, tracking scores, or tallying votes. It uses ten digits: 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9.In this 6 min read Computer Arithmetic | Set - 2 FLOATING POINT ADDITION AND SUBTRACTION FLOATING POINT ADDITION To understand floating point addition, first we see addition of real numbers in decimal as same logic is applied in both cases. For example, we have to add 1.1 * 103 and 50. We cannot add these numbers directly. First, we need to align 4 min read Like