Prezantimi i numrave ne informatike - Number Representation and Computer Arithmetic

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 1

Number Representation and Computer Arithmetic

Article to appear in Encyclopedia of Information Systems, Academic Press, 2001.

*********** The following outline is not part of the article *********** # pages

Introductory paragraph 0.5

I Natural Numbers 4

Brief history of number representation,

positional number systems, number radix conversion

II Digit Sets and Encodings 1.5

Binary, BCD, ASCII, standard or nonstandard, irredundant or redundant

III Integers 2.5

Representing negative whole numbers, signed magnitude,

1’s- and 2’s-complement numbers, biasing

IV Counting 1.5

Incrementers and decrementers, synchronous counters,

asynchronous counters, ultrafast counters

V Fixed-Point Numbers 3

Radix point, radix conversion for fractions, range and precision

VI Addition and Subtraction 3

Half- and full-adders, digit-serial adders, ripple-carry adders,

2’s-complement addition

VII Fast Adders 3.5

Carry recurrence, carry-lookahead adders, other fast adders

VIII Multiplication 3

Shift-add multiplication algorithms, programmed multiplication,

hardware binary multipliers, high-radix multipliers

IX Fast Multipliers 2.5

Tree, partial-tree, and array multipliers

X Division 2.5

Shift-subtract division algorithms, programmed division,

hardware binary dividers, faster dividers, shared multiply/divide

XI Real Numbers 2.5

Floating-point numbers, ANSI/IEEE standard, rounding modes

XII Floating-Point Arithmetic 3.5

Addition, subtraction, multiplication, and division

of floating-point numbers, floating-point hardware

XIII Function Evaluation 2.5

Square root, trig functions, logarithms, exponentiation,

approximating functions, table lookup

XIV Precision and Errors 3

Representation and computation errors, error accumulation,

surprises in floating-point arithmetic, certifiable arithmetic

XV Speed and Reliability 3

Ultrafast arithmetic circuits, pipelining techniques,

fault tolerance, error checking

XVI Unconventional Number Systems 3.5

Exotic representations, redundancy in arithmetic,

rational numbers, logarithmic numbers, RNS

XVII Research Directions 1

Further Reading 0.5

******************* end of outline ********************* Total pages 47

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 2

Arithmetic is a branch of mathematics that deals with numbers and numerical

computation. Arithmetic operations on pairs of numbers x and y include addition,

producing the sum s = x + y, subtraction, yielding the difference d = x – y, multiplication,

resulting in the product p = x × y, and division, generating the quotient q = x / y (and, in

the case of integer division, the remainder z = x mod y). Subtraction and division can be

viewed as operations that undo the effects of addition and multiplication, respectively.

Computer arithmetic is a branch of computer engineering that deals with methods of

representing integers and real values (e.g., fixed- and floating-point numbers) in digital

systems and efficient algorithms for manipulating such numbers by means of hardware

circuits or software routines. On the hardware side, various types of adders, subtractors,

multipliers, dividers, square-rooters, and circuit techniques for function evaluation are

considered. Both abstract structures and technology-specific designs are dealt with.

Software aspects of computer arithmetic include complexity, error characteristics,

stability, and certifiability of computational algorithms.

I Natural Numbers

When we think of numbers, it is usually the natural numbers that first come to our mind;

the type of numbers that sequence book or calendar pages, mark clock dials, flash on

stadium scoreboards, and guide deliveries to our houses. The set {0, 1, 2, 3, . . .} of

natural numbers, also known as whole numbers or unsigned integers, forms the basis of

arithmetic. We use natural numbers for counting and for answering questions that ask

“how many?”.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 3

Four-thousand years ago, Babylonians knew about natural numbers and were proficient

in arithmetic. Since then, representations of natural numbers have advanced in parallel

with the evolution of language. Ancient civilizations used sticks and pebbles to record

inventories or accounts. When the need for larger numbers arose, the idea of grouping

sticks or pebbles simplified counting and comparisons; for example, 27 was represented

by five groups of five sticks, plus two sticks. Eventually, objects of different shapes or

colors were used to denote such groups, leading to more compact representations.

Numbers must be differentiated from their representations, sometimes called numerals.

For example, the number “twenty-seven” can be represented in different ways using

various numerals or numeration systems; these include:

||||| ||||| ||||| ||||| ||||| || sticks or unary code

27 radix-10 or decimal code

11011 radix-2 or binary code

XXVII Roman numerals

However, we don’t always make the distinction between numbers and numerals and may

use “decimal numbers” in lieu of “decimal numerals.”

Roman numerals were used in Europe during the Middle Ages. Even when Europeans

learned that the Arabs had a better way of representing numbers, it took them centuries to

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 4

adopt the Arabic system based on numerals, or digits, 0-9 and a radix of 10. In these

decimal numbers, the worth of each position is 10 times that of the adjacent position to its

right, so that the string of digits “5327” represents five thousands, plus three hundreds,

plus two tens, plus seven ones.

Other radices have also appeared over the ages (see A. Glaser’s History of Binary and

Other Nondecimal Numeration, Tomash Publishers, 1981). Babylonians used radix-60

numbers, which make dealing with time easy. The radices 12 (duodecimal) and 5

(quinary) have also been used. The use of radix-2 ( binary) numbers became popular with

the onset of electronic computers, because their use of binary digits, or bits, having only

two possible values 0 and 1, is compatible with electronic signals. Radix-8 (octal) and

radix-16 (hexadecimal) numbers have been used as shorthand notation for binary

numbers. For example, a 24-bit binary number can be represented as an 8-digit octal or a

6-digit hexadecimal number by taking the bits in groups of threes and fours, respectively.

In a general radix-r positional number system, with a fixed word width of k, a number x is

represented by a string of k digits xi, with 0 ≤ xi ≤ r – 1:

x = Ói=0 to k–1 xi ri = (xk–1xk–2 . . . x1x0) r (1)

For example:

27 = (1 × 24) + (1 × 23) + (0 × 22) + (1 × 21) + (1 × 20) = (11011)two

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 5

In a k-digit radix-r number system, natural numbers from 0 to rk – 1 can be represented.

Conversely, given a desired representation range [0, M – 1], the required number k of

digits in radix r is obtained from the following equation:

k = ⎡logr M⎤ = ⎣logr(M – 1)⎦ + 1 (2)

For example, representing the decimal number 3125 requires 12 bits in radix 2, five digits

in radix 5, and four digits in radix 8.

Given a number x represented in radix r, one can obtain its radix-R representation in two

ways. If we wish to perform arithmetic in the new radix R, we simply evaluate a

polynomial in r whose coefficients are the digits xi. This corresponds to Equation (1) and

can be performed more efficiently by using Horner’s rule which involves repeated steps

of multiplying by r followed by addition:

(xk–1xk–2 . . . x1x0) r = ((…(xk–1r + xk–2)r + . . . x1)r + x0) (3)

This method is particularly suitable for manual conversion from an arbitrary radix r to

radix 10, given the relative ease with which we can perform radix-10 arithmetic.

To perform the radix conversion using arithmetic in the old radix r, we repeatedly divide

the number x by the new radix R, keeping track of the remainder in each step. These

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 6

remainders correspond to the radix-R digits Xi, beginning from X0. For example, we

convert the decimal number 23 to radix 2 as follows:

23 divided by 2 yields 11 with remainder 1

11 divided by 2 yields 5 with remainder 1

5 divided by 2 yields 2 with remainder 1

2 divided by 2 yields 1 with remainder 0

1 divided by 2 yields 0 with remainder 1

Reading the computed remainders from bottom to top, we find 23 = (10111)two. Using the

same process, we can convert 23 to radix 5 to get 23 = (43)five.

II Digit Sets and Encodings

The standard digit set used for radix-r numbers is {0, 1, . . . , r – 1}. This digit set leads to

a unique representation for every natural number. The binary or rdix-2 digit set is {0, 1}

which is conveniently representable by electronic signals. Typically, low voltage is used

to represent 0 and high voltage denotes 1, but the reverse polarity can also be used.

Conceptually, the decimal digit values 0 through 9 could be represented by 10 different

voltage levels. However, encoding everything with 0s and 1s makes it easier for

electronic circuits to interpret and process the information speedily and reliably.

One way to encode decimal digits using binary signals is to encode each of the digits 0-9

by means of its 4-bit binary representation. The resulting binary-coded decimal (BCD)

representation is shown below:

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 7

Digit BCD representation

0 0 0 0 0

1 0 0 0 1

2 0 0 1 0

3 0 0 1 1

4 0 1 0 0

5 0 1 0 1

6 0 1 1 0

7 0 1 1 1

8 1 0 0 0

9 1 0 0 1

The use of digit values 0 through r – 1 in radix r is just a convention. We could use more

than r digit values (for example, digit values –2 to 2 in radix 4) or use r digit values that

do not start with 0 (for example, digit set {–1, 0, 1} in radix 3). In the first instance, the

resulting number system possesses redundancy in that some numbers will have multiple

representations. More on this in Section XVI.

Of course, any finite set of symbols, not just digits, can be encoded using binary signals.

The American Standard Code for Information Interchange (ASCII) is one such

convention that represents upper- and lower-case letters, numerals, punctuation marks,

and other symbols in an 8-bit byte. For example, the 8-bit ASCII codes for the ten

decimal digits are of the form 0011xxxx, where the “xxxx” part is identical to the BCD

code discussed earlier. ASCII digits take twice as much space as BCD digits and thus are

not used in arithmetic units. Even less compact than ASCII is the 16-bit Unicode which

can accommodate symbols from many different languages.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 8

III Integers

The set { . . . , –3, –2, –1, 0, 1, 2, 3, . . . } of integers is also referred to as signed or

directed (whole) numbers. The most straightforward representation of integers consists of

attaching a sign bit to any desired representation of natural numbers, leading to signedmagnitude

representation. The standard convention is to use 0 for positive and 1 for

negative and attach the sign bit to the left end of the magnitude. Here are two examples:

+27 in 8-bit signed-magnitude binary code 00011011

–27 in 2-digit decimal code with BCD digits 1 0010 0111

Another option for encoding signed integers in the range [–N, P] is the biased

representation. If we add the positive value N (the bias) to all numbers in the desired

range, unsigned integers in the range [0, P + N] result. Any method for representing

natural numbers in [0, P + N] can then be used for representing the original signed

integers in [–N, P]. This type of biased representation has only limited application in

encoding of the exponents in floating-point numbers (see Section XI).

By far the most common machine encoding of signed integers is the 2’s-complement

representation. In the k-bit 2’s-complement format, a negative value –x, with x > 0, is

encoded as the unsigned number 2k – x. Figure 1 shows encodings of positive and

negative integers in the 4-bit 2’s-complement format. Note that the positive integers 0

through 7 (or 2k–1 – 1, in general) have the standard binary encoding, whereas negative

values –1 through –8 (or –2k–1, in general) have been transformed to unsigned values by

adding 16 (or 2k, in general) to them.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 9

0000

1111 0001

1110 0010

1101 0011

1100 0100

1000

1011 0101

1010 0110

1001 0111

-1

-5

-2

-3

-4

-8

-7

-6

_ +

Fig. 1. Schematic representation of 4-bit 2’s-complement code for integers in [–8, +7].

Two important properties of 2’s-complement numbers are worth noting. First, the

leftmost bit of the representation acts a the sign bit (0 for positive values, 1 for negative

ones). Second, the value represented by a particular bit pattern can be derived without a

need to follow different procedures for negative and positive values. We simply use

Equation (1), as we did for unsigned integers, except that the sign bit is considered to

have a negative weight. Here are two examples:

(01011)2’s-compl = (–0 × 24) + (1 × 23) + (0 × 22) + (1 × 21) + (1 × 20) = +11

(11011)2’s-compl = (–1 × 24) + (1 × 23) + (0 × 22) + (1 × 21) + (1 × 20) = –5

The reason for the popularity of 2’s-complement representation will become clear when

we discuss addition and subtraction in Section VI.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 10

Other complement representation systems can also be devised, but none is in widespread

use. Choosing any complementation constant M, that is at least as large as N + P + 1,

allows us to represent signed integers in the range [–N, P], with the positive numbers in

[0, +P] corresponding to the unsigned values in [0, P] and negative numbers in [–N, –1]

represented as unsigned values in [M – N, M – 1]. Sometimes, M itself is used as an

alternate code for 0 (actually, –0). For example, the k-bit 1’s-complement system is based

on M = 2k – 1 and includes numbers in the range [–(2k/2 – 1), 2k/2 – 1], with 0 having two

representations: the all-0s string and the all-1s string.

IV Counting

The natural numbers are ordered. Each natural number x has a successor succ(x), which is

the next number in this order and, except when x = 0, it has a predecessor pred(x).

Counting is the act of going through the successor or predecessor sequence, beginning

with some initial value. So, (3, 4, 5, 6, . . . ) is an up-counting sequence and (15, 14, 13,

12, . . . ) is a down-counting sequence (for a detailed treatment of various types of

counters, see R.M.M. Oberman’s Counting and Counters, Macmillan, 1981). Hardware

circuits that mimic this process, advancing to the successor number or retrogressing to the

predecessor each time a count control signal is asserted, are known as up-counters and

down-counters, respectively. A k-bit modulo-2k down-counter would go into negative

2’s-complement values if it is decremented past zero. An up/down-counter can go in

either direction, depending on the value of a direction control signal.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 11

In what follows, we focus on up-counters that hold unsigned values. Asynchronous

counters can be built of edge-triggered storage elements (flip-flops) and nothing else. The

more commonly used synchronous counters are built of a register, a hardware

incrementer, and some logic that allows the incremented count or an initial value to be

loaded into the register when appropriate control signals are asserted. Figure 2 shows the

block diagram for a synchronous counter.

Count register

Mux

Incrementer

Input

Load

Incr / Init

___

x + 1

0 1

Fig. 2. Synchronous binary counter with initialization capability.

The counter design shown in Fig. 2 is adequate for most applications. It can be made

faster by using a fast incrementer with carry-lookahead feature similar to that used in fast

adders, to be discussed in Section VII. If still higher speed is required, the counter can be

divided into blocks. A short initial block (say, 3 bits wide) can easily keep up with the

fast incoming signals. Increasingly wider blocks to the left of the initial block need not be

as fast because they are adjusted less and less frequently.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 12

V Fixed-Point Numbers

A fixed-point number consists of a whole or integral part and a fractional part, with the

two parts separated by a radix point (decimal point in radix 10, binary point in radix 2,

and so on). The position of the radix point is almost always implied and thus the point is

not explicitly shown. If a fixed-point number has k whole digits and l fractional digits, its

value is obtained from the formula:

x = Ói=–l to k–1 xi ri = (xk–1xk–2 . . . x1x0 . x–1x–2 . . . x–l) r (4)

In other words, the digits to the right of the radix point are given negative indices and

their weights are negative powers of the radix. For example:

2.375 = (1 × 21) + (0 × 20) + (0 × 2–1) + (1 × 2–2) + (1 × 2–3) = (10.011)two

In a (k + l)-digit radix-r fixed-point number system with k whole digits, numbers from 0

to rk – r–l, in increments of r–l, can be represented. The step size or resolution r–l is often

referred to as ulp, or unit in least position. For example, in a (2 + 3)-bit binary fixed-point

number system, we have ulp = 2–3 and the values 0 = (00.000)two through 22 – 2–3 = 3.875

= (11.111)two are representable. For the same total number k + l of digits in a fixed-point

number system, increasing k will lead to enlarged range of numbers, whereas increasing l

leads to greater precision. Therefore, there is a tradeoff between range and precision.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 13

Signed fixed-point numbers can be represented by the same methods discussed for signed

integers: signed-magnitude, biased format, and complement method. In particular, for

2’s-complement format, a negative value –x is represented as the unsigned value 2k – x.

Figure 3 shows encodings of positive and negative integers in the (1 + 3)-bit fixed-point

2’s-complement format. Note that the positive values 0 to 7/8 (or 2k–1 – 2–l, in general)

have the standard binary encoding, whereas negative values –1/8 to –1 (or –2–l to –2k–1, in

general) are transformed to unsigned values by adding 2 (or 2k, in general) to them.

0.000

1.111 0.001

1.110 0.010

1.101 0.011

1.100 0.100

1.000

1.011 0.101

1.010 0.110

1.001 0.111

+.125

+.25

+.375

+.5

+.625

+.75

+.875

-.125

-.625

-.25

-.375

-.5

-1

-.875

-.75

_ +

Fig. 3. Schematic representation of 4-bit 2’s-complement encoding for (1 + 3)-bit fixedpoint

numbers in the range [–1, +7/8].

The two important properties of 2’s-complement numbers, previously mentioned in

connection with integers, are valid here as well; namely, the leftmost bit of the number

acts a the sign bit, and the value represented by a particular bit pattern can be derived by

considering the sign bit as having a negative weight. Here are two examples:

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 14

(01.011)2’s-compl = (–0 × 21) + (1 × 20) + (0 × 2–1) + (1 × 2–2) + (1 × 2–3) = +1.375

(11.011)2’s-compl = (–1 × 21) + (1 × 20) + (0 × 2–1) + (1 × 2–2) + (1 × 2–3) = –0.625

Conversion of fixed-point numbers from radix r to another radix R is done separately for

the whole and fractional parts. Converting the whole part was discussed in Section I. To

convert the fractional part, we can again use arithmetic in the new radix R or in the old

radix r, whichever is more convenient. With radix-R arithmetic, we simply evaluate a

polynomial in r–1 whose coefficients are the digits xi. The simplest way to do this is to

view the fractional part as an l-digit integer, convert this integer to radix R, and divide the

result by rl.

To perform radix conversion using arithmetic in the old radix r, we repeatedly multiply

the fraction y by the new radix R, noting and removing the integer part in each step.

These integer parts correspond to the radix-R digits X–i, beginning from X–1. For example,

we convert .175 to radix 2 as follows:

.175 multiplied by 2 yields .350 with integer part 0

.350 multiplied by 2 yields .700 with integer part 0

.700 multiplied by 2 yields .400 with integer part 1

.400 multiplied by 2 yields .800 with integer part 0

.800 multiplied by 2 yields .600 with integer part 1

.600 multiplied by 2 yields .200 with integer part 1

.200 multiplied by 2 yields .400 with integer part 0

.400 multiplied by 2 yields .800 with integer part 0

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 15

Reading the recorded integer parts from top to bottom, we find .175 = (.00101100)two.

This equality is approximate because the result did not converge to 0. In general a

fraction in one radix may not have an exact representation in another radix. In any case,

we simply carry out the process above until the required number of digits in the new

radix have been obtained.

VI Addition and Subtraction

We will cover only integer addition and subtraction. Fixed-point numbers that are both in

the same format can be added like integers by simply ignoring the implied radix point.

When two bits are added, the sum is a value in the range [0, 2] which can be represented

by a sum bit and a carry bit. The circuit that can compute the sum and carry bits is known

as a half-adder (HA) with its truth table and symbolic representation shown in Fig. 4. The

carry output is the logical AND of the two inputs, while the sum output is the exclusive

OR (XOR) of the inputs. By adding a carry input to a half-adder, we get a binary fulladder

(FA) whose truth table and schematic diagram are depicted in Fig. 5.

x y c s

----------------

0 0 0 0

0 1 0 1

1 0 0 1

1 1 1 0

Inputs Outputs

x y

Fig. 4. Truth table and schematic diagram for a binary half-adder.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 16

x y c c s

----------------------

0 0 0 0 0

0 0 1 0 1

0 1 0 0 1

0 1 1 1 0

1 0 0 0 1

1 0 1 1 0

1 1 0 1 0

1 1 1 1 1

Inputs Outputs

c out c in

in out x

Fig. 5. Truth table and schematic diagram for a binary full-adder.

A full-adder, connected to a flip-flop for holding the carry bit from one cycle to the next,

functions as a bit-serial adder. The inputs of a bit-serial adder are supplied in synchrony

with a clock signal, one bit from each operand per clock cycle, beginning from the leastsignificant

bits. One bit of the output is produced per clock cycle and the carry from one

cycle is held and used as input in the next cycle. A ripple-carry adder, on the other hand,

unfolds this sequential behavior into space, using a cascade of k full-adders to add two kbit

numbers (Fig. 6).

c out c in

0 0

c 0

1 1

2 2

4 2

FA FA FA FA

Fig. 6. Four-bit ripple-carry binary adder.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 17

The ripple-carry design of Fig. 6 becomes a radix-r adder if each binary full-adder is

replaced by a radix-r full-adder that accepts two radix-r digits (each encoded in binary)

and a carry-in signal, producing a radix-r sum digit and a carry-out signal. Because the

carry signals are always binary and their propagation is independent of the radix r, in the

rest of this section and in Section VII, we do not deal with radices other than 2.

An adder/subtractor for 2’s-complement numbers can be built by adding a controlled

complementation circuit (a two-way multiplexer with y and its bitwise complement as the

two inputs) to an adder of any type. The resulting circuit is shown in Fig. 7. To justify

this design, note that x – y can be computed by forming the 2’s-complement of y and

adding it to x. However, the 2’s-complement of y, which is 2k – y, can be computed by

adding 1 to (2 k – 1) – y, the bitwise complement of y. The addition of 1 is accomplished

by inserting a carry-in of 1 into the adder when it is used to perform subtraction.

Mux

Adder

0 1

x y

y or y

add/sub

___

c in

Cont rolled

complementation

0 for addition,

1 for subtraction

Fig. 7. Two’s-complement adder/subtractor.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 18

VII Fast Adders

Ripple-carry adders are quite simple and easily expandable to any desired width.

However, they are rather slow, because carries may propagate across the full width of the

adder. This happens, for example, when the two 8-bit numbers 10101011 and 01010101

are added. Because each full-adder requires some time to generate its carry output,

cascading k such units together implies k times as much signal delay in the worst case.

This linear amount of time becomes unacceptable for wide words (say, 32 or 64 bits) or

in high-performance computers, though it might be acceptable in an embedded system

that is dedicated to a single task and is not expected to be fast.

A variety of fast adders can be designed that require logarithmic, rather than linear, time.

In other words, the delay of such fast adders grows as the logarithm of k. The best-known

and most widely used such adders are carry-lookahead adders. The basic idea in carrylookahead

addition is to form the required intermediate carries directly from the inputs

rather than from the previous carries, as was done in Fig. 6. For example, the carry c3 in

Fig. 6, which is normally expressed in terms of c2 using the carry recurrence

c3 = x2y2 + (x2 ⊕ y2)c2

can be directly derived from the inputs based on the logical expression:

c3 = x2y2 + (x2 ⊕ y2)x1y1 + (x2 ⊕ y2)(x1 ⊕ y1)x0y0 + (x2 ⊕ y2)(x1 ⊕ y1)(x0 ⊕ y0)c0

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 19

To simplify the rest of our discussion of fast adders, we define the carry generate and

carry propagate signals and use them in writing a carry recurrence that relates ci+1 to ci:

gi = xi yi

pi = xi ⊕ yi

ci+1 = gi + pi ci

The expanded form of c3, derived earlier, now becomes:

c3 = g2 + p2 g1 + p2 p1 g0 + p2 p1 p0 c0

It is easy to see that the expanded expression above would grow quite large for a wider

adder that requires c31 or c52, say, to be derived. A variety of lookahead carry networks

exist that systematize the preceding derivation for all the intermediate carries in parallel

and make the computation efficient by sharing parts of the required circuits whenever

possible. Various designs offer tradeoffs in speed, cost, VLSI chip area, and power

consumption. Information on the design of fast carry networks and other types of fast

adders can be found in books on computer arithmetic.

Here, we present just one example of a fast carry network. The building blocks of this

network are the carry operator which combines the generate and propagate signals for

two adjacent blocks [i + 1, j] and [h, i] of digit positions into the respective signals for the

wider combined block [h, j]. In other words

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 20

[i + 1, j] ¢ [h, i] = [h, j]

where [a, b] stands for (g[a,b], p[a,b]) representing the pair of generate and propagate

signals for the block extending from digit position a to digit position b. Because the

problem of determining all the carries ci is the same as computing the cumulative

generate signals g[0,i], a network built of ¢ operator blocks, such as the one depicted in

Fig. 9, can be used to derive all the carries in parallel. If a cin signal is required for the

adder, it can be accommodated as the generate signal g–1 of an extra position on the right.

¢ ¢ ¢ ¢

¢ ¢

¢ ¢ ¢

[7, 7 ] [6, 6 ] [5, 5 ] [4, 4 ] [3, 3 ] [2, 2 ] [1, 1 ] [0, 0 ]

[0, 7 ] [0, 6 ] [0, 5 ] [0, 4 ] [0, 3 ] [0, 2 ] [0, 1 ] [0, 0 ]

g[ 0 , 1 ] p [ 0,1]

g[ 1 , 1 ] p [ 1,1]

[0,0]

[2, 3 ]

[4, 5 ]

[6, 7 ]

[4, 7 ]

[0, 3 ]

[0, 1 ]

Fig. 9. Brent-Kung lookahead carry network for an 8-digit adder.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 21

For a k-digit adder, the number of operator blocks on the critical path of the carry

network exemplified by Fig. 9 is 2( ⎡log2 k⎤ – 1). Many other carry networks can be

designed that offer speed-cost tradeoffs.

An important method for fast adder design, that often complements the carry-lookahead

scheme, is carry-select. In the simplest application of the carry-select method, a k-bit

adder is built of a (k/2)-bit adder in the lower half, two (k/2)-bit adders in the upper half

(forming two versions of the k/2 upper sum bits with ck/2 = 0 and ck/2 = 1), and a

multiplexer for choosing the correct set of values once ck/2 becomes known. A hybrid

design, in which some of the carries (say, c8, c16, and c24 in a 32-bit adder) are derived via

carry-lookahead and are then used to select one of two versions of the sum bits that are

produced for 8-bit blocks concurrently with the operation of the carry network, is quite

popular in modern arithmetic units.

VIII Multiplication

The simplest machine multipliers are designed to follow a variant of the pencil-and-paper

multiplication algorithm depicted in Fig. 9, where each row of dots in the partial

products bit-matrix is either all 0s (if the corresponding yi = 0) or the same as x (if yi = 1).

When we perform a k × k multiplication manually, we form all of the k partial products

and add the resulting k numbers to obtain the product p.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 22

o o o o

---------------

o o o o

---------------

o o o o o o o o

Operands

Partial

products

bit-matrix

Fig. 9. Multiplication of 4-bit numbers in dot notation.

For machine execution, it is easier if the cumulative partial product is initialized to 0,

each row of the bit-matrix added to it as the corresponding term is generated, and the

result of addition shifted to the right by one bit to achieve proper alignment with the next

term, as depicted in Fig. 9. In fact, this is exactly how programmed multiplication is

performed on a machine that does not have a hardware multiply unit. The recurrence

equation describing the process above is:

p(j+1) = (p(j) + yj x 2k) 2–1 with p(0) = 0 and p(k) = p (5)

|––– add –––|

|–– shift right ––|

Because by the time we are done, the right shifts will have caused the first partial product

to be multiplied by 2–k, we pre-multiply x by 2k to offset the effect of these right shifts.

This is not an actual multiplication but is done by aligning x with the upper half of the 2kbit

cumulative partial product in the addition steps. Figure 10 depicts a possible hardware

realization of the foregoing shift-add multiplication algorithm. The shifting of the partial

product need not be done in a separate step but can be incorporated in the connecting

wires that go from the adder output to the doublewidth register.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 23

After k iterations, recurrence (5) leads to:

p(k) = xy + p(0)2–k

Thus if p(0) is initialized to 2kz (z padded with k zeros) instead of 0, the expression xy + z

will be evaluated. This multiply-add operation is quite useful for many applications and

is performed at essentially no extra cost compared to plain shift-add multiplication.

Multiplier y

Mux

Adder

c out

0 1

Doublewidth partial product p

Multiplicand x

Shift

(j)

yj x

Fig. 10. Hardware multiplier based on the shift-add algorithm.

The preceding bit-at-a-time multiplication scheme can be easily extended to a digit-at-atime

algorithm in a higher radix such as 4, 8, or 16. In this case, the multiplexer in Fig. 10

(which is really a bit-by-number multiplier) must be replaced with a digit-by-number

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 24

multiplier circuit (perhaps implemented as a multioperand adder), the single-bit shifts

replaced by h-bit shifts for radix-2h algorithm, and the number of iterations reduced to

from k to k/h. These faster high-radix multipliers may still be too slow for some

applications. In such cases, fully combinational tree multipliers are used in which the

addition of the partial products bit-matrix is done by means of a tree-structured

combinational circuit.

IX Fast Multipliers

Instead of developing the partial products one at a time in radix 2 or in radix 2h, we can

form all of them simultaneously, thus reducing the multiplication problem to n-operand

addition, where n = k in radix 2, n = k/2 in radix 4, and so on. For example, a 16 × 16

multiplication becomes a 16-operand addition problem in radix 2 or an 8-operand

addition problem in radix 4.

In tree multipliers, the n operands thus formed are added in two stages. In stage 1, a tree

built of carry-save adders or similar compression circuits is used to reduce the n

operands to two operands that have the same sum as the original n numbers. A carry-save

adder (see Section XVI) reduces three values to two values, for a reduction factor of 1.5,

thus leading to a ⎡log1.5(n/2)⎤-level circuit for reducing n numbers to two. The two

numbers thus derived are then added by a fast logarithmic-time adder, leading to an

overall logarithmic latency for the multiplier (Fig. 11).

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 25

Such a full-tree multiplier is rather complex and its speed may not be needed for all

applications. In such cases, more economical partial-tree multipliers might be

implemented. For example, if about half of the partial products are accommodated by the

tree part, then two passes through the tree can be used to form the two numbers

representing the desired product, with the results of the first pass fed back to the inputs

and combined with the second set of partial products (Fig. 11). A partial-tree multiplier

can be viewed as a (very-) high-radix multiplier. For example, if 12 partial products are

combined in each pass, then a radix-212 multiplication is effectively performed.

Adder

Large tree of

carry-save

adders

. . .

All partial products

Product

Adder

Small tree of

carry-save

adders

. . .

Several partial products

Product

Logdepth

Fig. 11. Schematic diagrams for full-tree and partial-tree multipliers.

An array multiplier uses the same two-stage computation scheme of a tree multiplier,

with the difference that the tree of carry-save adders is one-sided (has the maximum

possible depth) and the final adder is of ripple-carry type (quite slow). An example 4 x 4

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 26

array multiplier is depicted in Fig. 12. HA and FA cells are half- and full-adders defined

in Figs. 4 and 5, respectively, and MA cells are modified full-adders, one of whose inputs

is internally formed as the logical AND of xi and yj.

The reader may well ask why such a slow tree-type multiplier is of any interest at all. The

answer is that array multipliers are quite suitable for VLSI realization, given their highly

regular design and efficient wiring pattern. They can also be readily pipelined by

inserting latches between some of the rows of cells, thus allowing several multiplications

to be performed on the same hardware structure.

x 2 x1 x0

x 3

p 2

p 1

p 0

p7 p6 p5 p4

FA FA HA

MA MA MA MA

Fig. 12. Array multiplier for 4-bit unsigned operands.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 27

X Division

Like multipliers, the simplest machine dividers are designed to follow a variant of the

pencil-and-paper division algorithm depicted in Fig. 13, where each row of dots in the

subtracted bit-matrix is either all 0s (if the corresponding qi = 0) or the same as y (if qi =

1). When we perform a 2k / k division manually, we form the subtracted terms one at a

time by “guessing” the value of the next quotient digit, subtract the appropriate term (0 or

a suitably shifted version of y) from the partial remainder (initialized to the value of the

dividend x), and proceed until all k bits of q have been determined. At this time, the

partial remainder becomes the final remainder z.

o o o o

----------------

o o o o | o o o o o o o o

o o o o

---------------

o o o o

Dividend

Subtracted

bit-matrix

z Remainder

q Quotient

y Divisor

Fig. 13. Division of an 8-bit number by a 4-bit number in dot notation.

For hardware or software implementation, a recurrence equation describing the process

above is used:

z(j+1) = 2 z(j) – qk–j y 2k with z(0) = x and z(k) = 2kz (6)

|shift|

|–– subtract ––|

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 28

Because by the time we are done, the left shifts will have caused the partial remainder to

be multiplied by 2k, the true remainder is obtained by multiplying the final partial

remainder by 2–k (shifting it to the right by k bits). Figure 14 depicts a possible hardware

realization of the foregoing shift-subtract division algorithm. As in multiplication, the

shifting of the partial remainder need not be done in a separate step but can be

incorporated in the wires connecting the adder output to the partial remainder register.

Quotient q

Mux

Adder

c out

0 1

Partial remainder z (ini tial value x)

Divisor y

Shift

Quotient

digit

selector

Load

ci n

(j)

Fig. 14. Hardware divider based on the shift-subtract algorithm.

A comparison of Figs. 10 and 14 reveals that multipliers and dividers are quite similar

and can be implemented with shared hardware within an arithmetic/logic unit (ALU) that

performs different operations based on an externally supplied function code.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 29

As in the case of multipliers, high-radix dividers speed up the division process by

producing several bits of the quotient in each cycle. Whereas there is no counterpart to

fast tree multipliers for performing division, array dividers do exist and are structurally

quite similar to the array multiplier of Fig. 12. It is also possible to perform division by

using a sequence of multiplications instead of additions. Even though multiplications are

slower and costlier to implement than additions, advantage over additive schemes may be

gained because far fewer multiplications are needed to perform division. Details can be

found in any book on computer arithmetic.

XI Real Numbers

Integers in a prescribed range can be represented exactly for automatic processing, but

most real numbers must be approximated within the machine’s finite word width. Some

real numbers can be represented as, or approximated by, (k + l)-bit fixed-point numbers

(see Section V). A problem with fixed-point representations is that they are not very good

for dealing with very large and extremely small numbers at the same time. Consider the

two (8 + 8)-bit fixed-point numbers shown below:

x = (0000 0000 . 0000 1001)two Small number

y = (1001 0000 . 0000 0000) two Large number

The relative representation error due to truncation or rounding of digits beyond the –8th

position is quite significant for x, but it is much less severe for y. On the other hand,

neither y2 nor y / x is representable in this number format.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 30

The most common representation of numbers for computations dealing with values in a

wide range is the floating-point format. Old computers used many different floating-point

number formats, and some specialized machines still do, but for the most part, the IEEE

floating-point standard format (ANSI/IEEE Standard 754-1985; available from IEEE

Press) has been adopted by the computer industry. We thus formulate our discussion of

floating-point numbers and arithmetic exclusively in terms of this standard format. Other

formats will differ in their parameters and representation details, but the basic tradeoffs

and algorithms remain the same.

A floating-point number has three components: sign ±, exponent e, and significand s,

together representing the value ±2es. The exponent is a signed integer represented in

biased format (a fixed bias is added to it to make it into an unsigned number). The

significand is a fixed-point number in the range [1, 2). Because the binary representation

of the significand always starts with “1.”, this fixed 1 is hidden and only the fractional

part of the significand is explicitly represented.

Figure 15 shows the details of short (32-bit) and long (64-bit) floating-point formats. The

short format has adequate range and precision for most common applications

(magnitudes ranging from 1.2 × 10–38 to 3.4 × 1038). The long format is used for highly

precise computations or those involving extreme variations in magnitude (from about 2.2

× 10–308 to 1.8 × 10308). Of course in both of these formats, as explained thus far, zero has

no proper representation. To remedy this problem, and to be able to represent certain

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 31

other special values, the smallest and largest exponent codes (all 0s and all 1s in the

biased exponent field) are not used for ordinary numbers. An all-0s word (0s in sign,

exponent, and significand fields) represents +0; similarly, –0 and ±∞ have special

representations, as does any nonsensical or indeterminate value, known as “not a

number” (NaN). Certain other details of this standard are beyond the scope of this article.

Short (32-bit) format

Long (64-bit) format

Sign Exponent Significand

8 bits,

bias = 127,

-126 to 127

11 bits,

bias = 1023,

-1022 to 1023

52 bits for fractional part

(plus hidden 1 in integer part)

23 bits for fractional part

(plus hidden 1 in integer part)

Fig. 15. The ANSI/IEEE standard floating-point formats.

When an arithmetic operation produces a result that is not exactly representable in the

format being used, the result must be rounded to some representable value. The

ANSI/IEEE standard prescribes four rounding options. The default rounding mode is

“round to nearest even”: choose the closest representable value and, in case of a tie,

choose the value with its least-significant bit 0. There are also three directed rounding

modes: “round toward +∞” (choose the next higher value), “round toward –∞” (choose

the next lower value), and “round toward 0” (choose the closest value that is less than the

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 32

value at hand in magnitude). With the round-to-nearest option, the maximum rounding

error is 0.5 ulp, while with directed rounding schemes, the error can be up to 1 ulp.

XII Floating-Point Arithmetic

The floating-point operations of multiplication and division are not much different from

their fixed-point counterparts. For multiplication, exponents of the two operands are

added and their significands are multiplied:

(±2e1s1) × (±2e2s2) = ±2e1+e2(s1 × s2)

Thus, a hardware floating-point multiplier consists of a significand multiplier and an

exponent adder that together compute 2es, with e = e1 + e2 and s = s1 × s2. The result’s

sign is easily obtained from the signs of the operands. This is not all, however. With s1

and s2 in [1, 2), their product will lie in [1, 4) and may thus be outside the permitted

range. If the product of the two significands is in [2, 4), dividing it by 2 via a 1-bit right

shift will put it back into the desired range. When this normalization is needed, the

exponent e must be incremented by 1 to compensate for the division of s by 2.

Floating-point division is similar and is governed by the equation:

(±2e1s1) / (±2e2s2) = ±2e1–e2(s1 / s2)

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 33

Again, given that the ratio s1 / s2 of the two significands lies in (0.5, 2), normalization

may be required for results that are less than 1. This normalization consists of multiplying

the significand by 2 via a 1-bit left shift and decrementing the resulting exponent by 1 to

compensate for the doubling of the significand. Of course, throughout the operation and

ensuing adjustments, for both multiplication and division, the hardware must check for

exceptions such as overflow (exponent too large) and underflow (exponent too small).

We next discuss floating-point addition. Subtraction can be converted to addition by

changing the sign of the subtrahend. To perform addition, the exponents of the two

operands must be equalized, if needed. Consider the addition of ±2e1s1 and ±2e2s2 , with

e1 > e2. By making both exponents equal to e1, the addition becomes:

(±2e1s1) + (±2e1(s2/2 e1–e2)) = ±2e1(s1 ± s2/2 e1–e2)

We thus see that s2 must be right-shifted by e1 – e2 bits before being added to s1. This

alignment shift is also called preshift (so named to distinguish it from the postshift needed

for normalizing a floating-point result). Figure 16 shows a complete example of floatingpoint

addition, including preshift, addition of aligned significands, and final rounding. In

this example, no postshift is needed because the result is already normalized. In general,

though, the result may need a 1-bit right shift, when it is in [2, 4), or a multibit left shift

when the addition of operands with different signs leads to cancellation or loss of

significance and one or more leading 0s appear in the result.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 34

Operands after alignment shi ft:

x = 2 1.00101101

y = 2 0.000111101101

Numbers to be added:

x = 2 1.00101101

y = 2 1.11101101

Extra bits to be

rounded off

Operand with

smaller exponent

to be preshifted

Result of addition:

s = 2 1.010010111101

s = 2 1.01001100

5 Rounded sum

Fig. 16. Alignment shift and rounding in floating-point addition.

A simplified block diagram for a hardware floating-point adder is shown in Fig. 17. It is

seen that once the operands are unpacked, their exponents and significands are processed

in two separate tracks whose functions are coordinated by the block labeled “Control &

sign logic.” For example, based on the difference of the two exponents, this unit decides

which operand must be preshifted. To economize on hardware, usually only one

preshifter is provided, say for the left operand of the adder. If the other operand needs to

be preshifted, the operands are physically swapped. Also, in adding operands with unlike

signs, the operand that is not preshifted is complemented. This time-saving strategy may

lead to the computation of y – x when in fact x – y is the desired result. The control unit

corrects this problem by forcing the complementation of the result if needed. Finally,

normalization and rounding are preformed and the exponent is adjusted accordingly.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 35

Normalize

& round

Add

Align

significands

Possible swap

& complement

Unpack

Cont rol

& sign

logic

Add/

Sub

Pack

Inputs

Output

Signs Exponents Significands

Sign Exponent Significand

x y

Sub

Add

Mux

Fig. 17. Simplified schematic of a floating-point adder.

XIII Function Evaluation

In many numeric calculations, there is a need to evaluate functions such as square-root,

logarithm, sine, or tangent. One approach to function evaluation is the use of an

approximating function that is easier to evaluate than the original function. Polynomial

approximations, derived from Taylor-series and other expansions, allow function

evaluation by means of addition, multiplication, and division. Here are a few examples:

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 36

ln x = 2(z + z3/3 + z5/5 + z7/7 + . . . ) where z = (x – 1)/(x + 1)

ex = 1 + x/1! + x2/2! + x3/3! + x4/4! + . . .

cos x = 1 – x2/2! + x4/4! – x6/6! + x8/8! – . . .

tan–1 x = x – x3/3 + x5/5 – x7/7 + x9/9 – . . .

A second approach is convergence computation: begin with a suitable approximation and

proceed to refine the value with iterative evaluation. For example, if q(0) is an

approximation to the square root of x, the following recurrence can be used to refine the

value, using one addition, one division, and a 1-bit right shift per iteration:

q(i+1) = 0.5(q(i) + x/q(i))

The initial approximation can be obtained via table lookup based on a few high-order bits

of x or simply be taken to be a constant. As an example, suppose that we want to use this

method to extract the square root of a floating-point number. To do this, we must halve

the exponent and find the square root of the significand. If the exponent is odd, we can

subtract 1 from it and double the significand, before applying this method. As a result, the

adjusted significand will be in [1, 4). We may thus take 1.5 as our initial approximation.

The better the initial approximation, the fewer the number of iterations needed to achieve

a certain level of accuracy. A special case of convergence computation is when each

iteration leads to the determination of one digit of the result, beginning with the mostsignificant

digit. Such digit recurrence schemes are similar in nature to shift-subtract

division discussed in Section X.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 37

There are many other methods for function evaluation. As our third and final example,

we consider a method based on lookup tables which is becoming increasingly popular,

given that tables can be implemented efficiently and compactly in VLSI technology.

Within a reasonably narrow interval [x(i), x(i+1)), a function f(x) can be approximated by

the linear function a + b(x – x(i)). This interpolation scheme leads to the hardware

implementation depicted in Fig. 18. The range of x values is divided into 2h intervals

based on the h high-order bits of x, which define the value xH. For each of these intervals

[xH, xH + 2–h), the corresponding a and b values of the approximating linear function a +

b(x – xH) = a + bxL are stored in two tables. Function evaluation with this method thus

involves two table accesses, one multiplication, and one addition.

Add

Table

for a

Output

Table

for b

Input x H L

f(x)

Multiply

h bits k - h bits

Fig. 18. Function evaluation by table lookup and linear interpolation.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 38

XIV Precision and Errors

Machine arithmetic is inexact for two different reasons. First, many numbers do not have

exact binary representations within a finite word format. This is referred to as

representation error. Second, even for values that are exactly representable, floatingpoint

arithmetic produces inexact results. For example, the exactly computed product of

two short floating-point numbers will have a 48-bit significand that must be rounded to fit

in 23 bits (plus the hidden 1). This is characterized as computation error.

It is important for both the designers of arithmetic circuits and for the users of machine

arithmetic to be mindful of these errors and to learn methods for estimating and

controlling them. There are documented instances of arithmetic errors leading to disasters

in computer-controlled critical systems. Even a small per-operation error of 0.5 ulp, when

accumulated over many millions, perhaps billions, of operations needed in some

applications, can lead to highly inaccurate or totally incorrect results.

We limit our discussion of errors, their sources, and countermeasures to a few examples

from floating-point arithmetic (see D. Goldberg’s article in “further reading” for more

detailed discussion and other examples).

One way to avoid excessive error accumulation is to carry extra precision in the course of

a computation. Even inexpensive calculators use extra digits that are invisible to the user

but help ensure greater accuracy for the results. Without these guard digits, the

computation of 1/3 will produce 0.333 333 333 3, assuming a 10-digit calculator.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 39

Multiplying this value by 3 will yield 0.999 999 999 9, instead of the expected 1. In a

calculator with two guard digits, the value of 1/3 is evaluated and stored as 0.333 333 333

333, but still displayed as 0.333 333 333 3. If we now multiply the stored value by 3, and

use rounding to derive the result to be displayed, the expected value 1 will be obtained.

Use of guard digits improves the accuracy of floating-point arithmetic but does not totally

eliminate some incorrect and highly surprising results. For example, floating-point

addition is not associative in that the algebraically equivalent computations (x + y) + z

and x + (y + z) may yield very different results. Similarly. Many other laws of algebra do

not hold for floating-point arithmetic, causing difficulties in result predictability and

certification. An optimizing compiler that switches the order of evaluation for the sake of

computation speed-up may inadvertently change the result obtained!

One of the sources of difficulties is loss of precision that occurs when subtraction is

performed with operands of comparable magnitudes. Such a subtraction produces a result

that is close to 0, making the effect of previous roundings performed on the operands

quite significant in relative terms. Such an event is referred to as catastrophic

cancellation. For example, when the algebraically correct equation

A = [s(s – a)(s – b)(s – c)]1/2

with s = (a + b + c)/2, is used to calculate the area of a needlelike triangle (a triangle for

which one side a is approximately equal to the sum b + c of the other two sides), a large

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 40

error can be produced due to the catastrophic cancellation in computing s – a. A user or

programmer who is aware of this problem can use an alternate formula that is not prone

to producing such large errors.

Because of the anomalies and surprises associated with floating-point arithmetic, there is

some interest in certifiable arithmetic. An example is offered by interval arithmetic

whereby each number is represented by a pair of values, a lower bound and an upper

bound. We represent x by the interval [xl, xu] if we are certain that xl ≤ x ≤ xu. Given

interval representations of x and y, arithmetic operations can be defined in such a way as

to ensure containment of the result in the interval that is produced as output. For example:

[xl, xu] + [yl, yu] = [xl + yl, xu + yu]

In this way, we always have a guaranteed error bound and will know when a result is too

imprecise to be trusted.

The ultimate in result certification is exact arithmetic, which may be feasible in some

applications through the use of rational numbers or other forms of exact representation.

For example, if each value is represented by a sign and a pair of integers for the

numerator and denominator, then numbers such as 1/3 will have exact representations

and an expression such as (1/3) × 3 will always produce the exact result. However,

besides limited applicability, exact rational arithmetic also implies significant hardware

and/or time overhead.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 41

XV Speed and Reliability

We would, of course, like to design arithmetic circuits to be as fast as possible. The adder

or multiplier must indeed be very fast if a machine is to execute one billion arithmetic

operations per second. We now have this level of performance on some desktop

computers, with 103 times greater performance in the largest available supercomputers,

and 106 times more being worked on in research laboratories. Therefore, methods for

speeding up arithmetic algorithms and their circuit implementations form an important

part of the field of computer arithmetic.

With modern VLSI technologies, design for high speed is a challenging undertaking. The

crossing of the gigahertz milestone in microprocessor clock rates signals the fact that

many hardware operations are occurring with subnanosecond latencies. Because an

electronic signal can travel only a few centimeters in one nanosecond, the roles of

interconnects and package boundary crossings are becoming increasingly important.

Given that clock distribution accounts for a significant portion of long wires and power

dissipation on a modern VLSI chip, there is significant incentive to do away with the

clocked or synchronous design style and adopt a fully asynchronous approach.

Speed is but one of several parameters that a designer must consider. In recent years,

compactness and power economy have emerged as important attributes of an

implementation. Design for compactness requires careful attention to implementation and

packaging technologies and their various constraints. One important aspect to consider is

the pin limitation at the chip and other levels of the hardware packaging hierarchy. Power

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 42

economy is somewhat related to compactness, but it also depends on signal transition

activity and circuit speed (faster circuit technologies use more power). Signal transition

activity can be reduced via judicious choice of encoding schemes and careful algorithm

design. Circuit speed can be reduced, while still keeping the same level of performance

through various architectural schemes.

A particularly useful method of designing high-throughput arithmetic circuits without a

need for ultrahigh-speed circuit elements is pipelining. We explain the use of pipelining

and the various tradeoffs involved in its implementation through an example. Consider

the array multiplier of Fig. 12. The critical (longest) signal path through this circuit goes

through four MA, one HA, and three FA blocks. This path begins at the upper left corner

of the diagram, proceeds diagonally to the lower right and then horizontally to the lower

left corner. Assuming, for simplicity, that all three block types have the same unit-time

latency, one multiplication can be performed every 8 time units.

By cutting the design of Fig. 12 in half, right below the last row of MA blocks, and

inserting temporary storage platforms (latches) to hold intermediate signal values at that

point, we can double the throughput of our multiplier. Once the intermediate signals from

one multiplication are stored in the latches, a second multiplication can begin in the upper

part of the circuit, while the lower part completes the first multiplication. Of course, if we

insert more latches, the throughput will increase even further. The improvement is not

indefinite, though, because the insertion of more latches introduces increasing overheads

in cost and time.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 43

When results of an arithmetic unit are critical to the safety of humans or success of a

mission, some form of checking must be performed. Design methods for fault-tolerant

arithmetic form active research areas. Here, we just mention one simple method based on

residue checking (for more information on this and other types of checking, see T.R.N.

Rao’s Error Codes for Arithmetic Processors, Academic Press, 1974).

Suppose that each value is represented by appending to it the residue modulo A, where A

is a suitably chosen check constant. Then addition can be checked in the manner shown

in Fig. 19. If the mod-A sum of the two check tags does not match the mod-A value of the

computed sum s, then the presence of an error is signaled. Special attention must be paid

to the design of the various components in Fig. 19 to ensure that matching of the two

residues implies fault-free operation with very high probability.

Add

x, x mod A

Add mod A

Compare Find

mod A

y, y mod A

s, s mod A Error

Not

equal

Fig. 19. Arithmetic unit with residue checking.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 44

XVI Unconventional Number Systems

Thus far, our discussion of computer arithmetic was based mostly on standard

representations that are widely used in digital systems; namely, 2’s-complement binary

and ANSI/IEEE floating-point format. Other number representation systems are either

invisible to the computer user (used to encode intermediate values for speeding up

arithmetic operations) or are applied in the design of application-specific systems. In this

section, we briefly review two examples of number systems in each of these categories.

These are only meant to give the reader a sense that other options exist (see B. Parhami’s

textbook in “further reading” for more detailed discussion and other examples).

The carry-save (or stored-carry) representation for binary numbers is extensively used in

the design of fast multipliers and other arithmetic circuits. A carry-save number is

essentially a pair of binary numbers whose sum is the value being represented. Given

such a carry-save value, it can be added to an ordinary binary number, yielding a carrysave

result at very high speed, because the addition does not involve any carry

propagation. Figure 20 shows the addition of a binary number x to a carry-save number

composed of y and z, yielding the carry-save number composed of s and c. Comparing

Fig. 20 to Fig. 6 reveals the origins of the name “carry-save”; note that here, unlike in

Fig. 6, carries are not connected to the next FA block but are saved along with the sum

bits to form a carry-save number.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 45

x y

y x

0 y 0

1 1

2 2

3 c 2

FA FA FA FA

z 3 z 2 z 1 z 0

c 4 c 3 c1

Fig. 20. Three binary numbers reduced to two numbers by a carry-save adder.

Carry-save numbers can be viewed as radix-2 numbers that use the digit set {0, 1, 2}.

From this observation, it is easy to generalize to other redundant representations such as

signed-digit numbers. For example, radix-8 numbers with the digit set [–5, 5] can be

added without carry propagation chains; the carry only affects the next higher position

and nothing else. Details are beyond the scope of this article.

The logarithmic number system (LNS) is an example of number representation for

application-specific systems. In LNS, a value x is represented by its sign and the

logarithm of its absolute value in some base. For example, if we use base-2 logarithms,

with 4 whole and 4 fractional bits, the numbers 8 and 11 are represented as 0011.0000

and 0011.0111, respectively. The key advantage of LNS is that it allows us to multiply or

divide numbers through addition or subtraction of their logarithms. For example, the

product and ratio of 11 and 8 are found as follows:

log2 11 + log2 8 = (0011.0111)two + (0011.0000)two = (0110.0111)two

log2 11 – log2 8 = (0011.0111)two + (0011.0000)two = (0000.0111)two

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 46

Addition and subtraction, on the other hand, become somewhat more complicated but

they are still manageable with help from lookup tables. Details are beyond the scope of

this article. Suffice it to say that practical applications of this representation have thus far

been limited to short word widths; however, available methods are improving and an

LNS with range equivalent to the short ANSI/IEEE floating-point format has recently

been implemented as part of a European microprocessor project.

The residue number system (RNS) has a long history dating back to the ancient Chinese.

Use of this representation method in computer arithmetic was proposed in the late 1950s.

Despite this long history, applications have remained limited due to the fact that RNS

makes some key arithmetic operations, such as division and magnitude comparison, quite

difficult. Its main advantages are simple addition and multiplication, thus making

applications in which these two operations are predominant more suitable for RNS

implementation. In RNS, each number is represented by a set of residues with respect to a

set of pairwise relatively prime moduli. For example, given the moduli set {3, 5, 7}, the

numbers 11 and 8 are represented by the set of their residues with respect to the moduli,

thus leading to the codes (2, 1, 4) and (2, 3, 1), respectively. The number of distinct

natural numbers that are represented is 3 × 5 × 7 = 105. This set of values can be used to

represent the natural numbers 0 to 104 or signed values –52 to +52.

In our example RNS with the moduli set {3, 5, 7}, adding or multiplying the numbers 11

and 8 is done by separately operating on the three residues, with each operation

performed modulo the corresponding modulus. We thus get:

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 47

(2, 1, 4) + (2, 3, 1) = (4 mod 3, 4 mod 5, 5 mod 7) = (1, 4, 5)

(2, 1, 4) × (2, 3, 1) = (4 mod 3, 3 mod 5, 4 mod 7) = (1, 3, 4)

Despite lack of widespread applications, both LNS and RNS remain important from a

theoretical standpoint. Additionally, there are indications that with advances in arithmetic

algorithms and VLSI technology, these two methods may find greater use in future (for

information on applications of RNS arithmetic in signal processing, see Residue Number

System Arithmetic, edited by M.A. Soderstrand, W.K. Jenkins, G.A. Jullien, and F.J.

Taylor, IEEE Press, 1986).

XVII Research Directions

Computer arithmetic has played a central role in the development of digital computers.

A.W. Burkes, H.H. Goldstine, and J. von Neumann, in their now classic 1946 report

entitled “Preliminary Discussion of the Logical Design of an Electronic Computing

Instrument,” set the stage for many ingenious schemes to perform fast arithmetic on early

digital computers, at a time when even the simplest circuits where bulky and expensive.

After more than half a century, research and development is still continuing unabated, for

even though many of the theoretical underpinnings of the field are now well understood,

new application challenges must be faced and old solution schemes must be adapted to

emerging technological constraints and opportunities.

Number Representation and Computer Arithmetic (B. Parhami / UCSB) 48

Examples of active research issues in computer arithmetic at the turn of the twenty-first

century include reducing power consumption, efficient handling of low-precision

multimedia data, subnanosecond operations, area-efficient implementations, configurable

processing elements, function evaluation with no error other than that dictated by the

mandatory rounding, certifiable arithmetic, compatibility/portability of numerical

software, applying novel computational paradigms, and fundamental theoretical limits.

New results in the field appear in Proceedings of the Symposia on Computer Arithmetic,

currently held in odd-numbered years, and in archival technical journals such as IEEE

Transactions on Computers, which occasionally devotes entire special issues to the topic.

Web resources can be accessed via the author’s home page at http://www.ece.ucsb.edu.

Detyra Kursi Anglisht per Universitete

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