Floating Point Arithmetic

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1 Floating Point Arithmetic Clark N. Taylor Department of Electrical and Computer Engineering Brigham Young University 1 Introduction Numerical operations are something at which digital logic excels. However, all of the arithmetic you have previously learned to compute in digital hardware assumes integers as inputs and outputs. While integers are useful for many applications, many real applications require either more range than an integer can give or more accuracy. In these course notes, I will first discuss how the binary point does not always have to lie at the end of the number stored, enabling more accurate representations of numbers even when performing integer computations. However, this fixed point notation requires intimate knowledge by the hardware designer of the numerical characteristics of the inputs. Floating point allows for more flexibility, at the cost of more hardware. We will discuss how floating point data types are implemented in hardware and how operations on these numbers are performed. Finally, we will briefly look at the IEEE standard floating point types that are used in microprocessors today. 2 Fixed Point Numbers Using fixed point numbers is simply a way to utilize integer operations (add, multiply, etc.) for real (technically, rational) numbers. To explain this concept, I will use decimal numbers as examples, but the same concepts work for binary. Let us consider a great example of fixed point numbers, our currency system. Every number expressed as currency has two place values after the decimal point. As an example, consider the dollar amounts $ and $ To add these together, we simply align the decimal points and add them together, yielding (1) Performing this operation in fixed point simply switches the base unit to pennies. So, rather than dollars, we have 10,035 pennies. The new 1

2 operation becomes: (2) Because I know that this final number represents the number of pennies, I can easily convert it back to dollars, yielding $122.52, the same answer found when adding dollars in Equation (1). As a general principle, addition operations with real numbers can be performed using integer operations, but the numbers have to be converted to integers first. The user of the integer addition needs to ensure that the radix points are aligned before addition. We illustrate this with two examples. To generalize on the previous example of currency, if we have two numbers with any finite number of places after the radix point, we can convert the two numbers into integers by multiplying by b N, where b is the base and N is the number of points after the radix point. So, if I want to add two decimal numbers and , I simply multiply both by 10,000 to create integers. I then add these two numbers together (13,522 and 26,877) to give my fixed point answer, 40,399. To convert back to the real answer, I divide by 10,000, yielding If the two numbers to be operated on do not have the same number of digits after the decimal point, then the largest of the two numbers must be chosen and used as my operator. For example, if I am adding 10.2 and , I cannot multiply one by 10 and the other by 1000 before performing the addition as that would not align the decimal points. Therefore, I multiply both by 1000, leading to 10, , 456. The sum (40,656) is then divided by 1000 to yield When using a multiplication operator, fixed point is actually easier to implement (conceptually). When performing multiplication, the radix points do not need to be aligned. Instead, the total number of points after the decimal must be remembered and applied at the end. For example, has three total numbers after the decimal point. Therefore, we multiply 1, = and divide the answer by 1000, yielding Once again the user of the operators is responsible for maintaining the number of places after the radix point to ensure the final numerical results are meaningful. 3 Floating Point Numbers While fixed point numbers are very useful (and, as a side note, are typically used in designs where minimizing area / maximizing clock speed are the primary objectives), they do require the user to keep track of the radix point of all incoming and outgoing operations. This makes it specific to a certain type of number coming in. For example, if designing hardware and we know that inputs are always going to be between -1 and 1, and the integer multiplier we are using is 16 bits, then I can always just multiply the inputs by 2 14 and have a 16 bit number. (2 bits are allocated to the range -1 to 1, the remaining 14 bits are for places after the radix point.) However, if the range of all inputs 2

3 s exponent mantissa Figure 1: A floating number with its three components: sign, exponent, and mantissa suddenly changes to between 0.0 and , note that the first 10 bits of the maximum value will always be zero. So, multiplying by 2 14 will not effectively use the top 12 bits of the multiplier as the top 12 bits will always be zero. Therefore, I would like to have the hardware determine, at the time of computation, what range it should use. To enable this flexibility, we will store a number with two parts. First, the mantissa or integer part of the number. Second, the exponent part which tells where the radix point should be in relationship to the integer. In addition, floating point mantissas are typically stored in sign-magnitude representation, so the mantissa is the magnitude and one other bit is used for the sign. The exponent on the other hand is usually signed. Figure 1 demonstrates a typical floating point number divided into sign, mantissa, and exponent. Each number that is stored in floating point can be written out in full form as: n = s m 2 e, (3) where n is the number represented by the floating point value, m is the value in the mantissa, and e is the value in the exponent. Note that in its current form, there is an ambiguity where the same number can be represented multiple different ways in the floating point representation. For example, the number 0.5 could be expressed by at least the following floating point representations. s m e (4) To overcome this ambiguity, the mantissa of a floating point number is defined as 1.xxxxxx. By forcing the mantissa to always have a 1 in the first digit and the binary point immediately afterwards, floating point representations of numbers become unique. In addition, because the first digit is 1, it does not actually need to be stored, essentially giving you an extra digit of precision in the mantissa without any storage cost. Now that we have the floating point representation defined, let us discuss how operations are performed on these numbers. First, let us consider addition. As shown before with fixed point numbers, the decimal points must be aligned before addition can occur. Therefore, the following steps are used for floating point addition. 3

4 1. The two exponents of the numbers are compared. We will call the number with the larger exponent A and the smaller exponent B. The output exponent is set to A s exponent. 2. B s mantissa is shifted so that the exponents would be the same. Note that this has to be expanded to include the implied 1 before the shifting operation occurs. 3. The two mantissas (one with the implied one pre-pended, the other after pre-pending and shifting) are added together using a sign-magnitude adder. 4. The result of this addition is shifted to be of the 1.xxxx format and the exponent from A is modified to record the shift. Let us walk through a simple example where we add together 7 and 3, represented in floating point. For making things simple, we will assume that the exponent is 3 bits and the mantissa is 5. The floating point representation for 7 will be s = 0, e = 010, m = and for 3 will be s = 0, e = 001, and m = To add these numbers together, the mantissas first have to be changed into their full form by adding a 1 at the beginning. Internally the two mantissas become for 7 and for 3. Because 7 has the larger exponent, we shift 3 s mantissa over one, becoming and set the preliminary output exponent to 010 (from 7). After adding the two mantissas, we obtain Note that some bit growth has occurred. Therefore, to set the left-most digit to one, we must increase the exponent by one, making the output exponent 011. Therefore, the final floating point number becomes s = 0, e = 011, m = There are several important facts to notice about floating point addition. Some of these include: 1. To align the mantissas and to normalize at the end requires a barrel shifter when implementing in hardware. Due to this extra hardware, floating point addition is a hardware-intensive operation. 2. Adjusting the mantissa to align the decimal points and when normalizing the result at the end can result in accuracy loses. 3. Subtracting two numbers of very similar magnitude can result in severe accuracy loses. For example, when subtracting 55 from 54, both numbers have 6 bits of accuracy to begin with, but will essentially end with only 1 bit of accuracy. By comparison, multiplication is conceptually much easier to implement in floating point. Because the mantissas do not need to be aligned prior to multiplication, the mantissas are simply multiplied together and the exponents added together. The result of the multiplication operation is normalized to place a one in the left-most digit. Note that because floating point numbers are stored in sign-magnitude format, determining the sign of the output in multiplication is trivial. 4

5 4 IEEE Floating Point In addition to understanding how general floating point numbers work as described in the previous section, it is also important to understand how the floating point numbers used in everyday computers work. To ensure that floating point operations were general across computers, floating point representations and their operations were standardized by the IEEE (IEEE Std 754). The float used in C is specified by the IEEE 754 standard to be a total of 32 bits. The first bit is the sign bit, the following 8 bits are the exponent bits, with the remaining 23 bits being used for the mantissa. For a double, there are a total of 64 bits, with the first bit being the sign bit, the next 11 bits for the exponent, and the remaining 52 bits for the mantissa. In general, the IEEE floating point numbers act the same as any floating point number described in the previous section. However, there are several unique differences that must be considered when implementing IEEE numbers. First, the exponent in the IEEE standard is not stored in 2 s component form as might be expected. Instead, all exponents are stored in an unsigned notation and have 2 e 1 1 ( where e is the size of the exponent) subtracted from them to yield the final exponent. So, for floats, each exponent stored has 127 subtracted from it, making the range of possible exponents -127 to 128. However, an exponent of 0 or 2 e 1 is used to denote special case numbers, leading to a range of -126 to 127. Similarly, for the 64 bit IEEE floating point number, each exponent has 1023 subtracted from it, leading to an effective range of to 1023 for the exponents. Throughout the rest of these notes, I will use the values for the 32-bit IEEE standard (e.g., -126 and 127), but the same principles apply for the 64-bit IEEE numbers. Second, there are several special cases defined in the IEEE standard that must be considered when implementing IEEE floating point operations. These are indicated by placing either a 0 or 2 e 1 (255) in the exponent field. The special cases and their representations are: Special Case Exponent Mantissa Zero 0 0 Really Small (Denormalized) Numbers 0 non-zero Infinities 2 e 1 0 Not A Number (NaN) 2 e 1 non-zero These special cases can be described as follows: Zeros is present because with the implied 1 before any value stored in the mantissa, 0 cannot technically be represented in floating point notation; only numbers really close to zero ( for 32 bit floating point numbers) can be represented. Therefore, a special case was designed to give a true representation of zero. Note, however, that both a positive and negative zero exist due to the use of the sign-magnitude form. Really Small (Denormalized) Numbers. To allow for numbers that are even smaller than 2 126, the IEEE standard allows for numbers that do 5

6 not have the implied 1 at the beginning. The mantissa then represents the true value times Therefore, with 23 bits of accuracy in the mantissa, it can represent numbers all the way down to Infinities and Not a Number (NaN) are used to capture overflow types of problems and prevent them from propagating with the user being aware. Infinities are generally assigned to outputs when the operations entails a very large number divided by a very small number (such that the exponent cannot be stored in e bits) or when an infinity is added to another number. NaN is generated when an illegal (or indeterminate) operation has occurred, such as the square root of a negative number, 0 divided by 0, or infinity minus infinity. Operations with Infinity or NaN as an input will generally output the special case as well, ensuring that bad numbers do not corrupt the computations. In addition to specifying special cases, the IEEE standard also specified how rounding should occur. Rounding occurs a number has to be placed in a certain length storage unit, but currently has more bits than the storage unit has available. Two examples of when this occurs which we have already discussed are (1) when the mantissa of a number is shifted right to align binary points, meaning that more bits of precision are available than can be added together; and (2) when the output of a multiply operation is turned into a single floating point number as something twice the width of the mantissa was generated by the multiply. When rounding, IEEE numbers round to the nearest digit (as you would expect.) Another special case specified by the IEEE standard is if the number being rounded is actually.5 (represented by a one right after the point being rounded to and all 0 s afterwards). In this case, it always rounds to the nearest even digit, leaving a 0 in the last digit of the mantissa. 5 Conclusion In these course notes, we have discussed fixed and floating point numbers and briefly introduced the hardware required to deal with floating point numbers. In addition, we have looked at the IEEE standard for floating point numbers and discussed some of the unique aspects of this standard. 6

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