 Open Access
 Total Downloads : 803
 Authors : Asiya Thapaswin Pattan, V.Ramesh
 Paper ID : IJERTV1IS7138
 Volume & Issue : Volume 01, Issue 07 (September 2012)
 Published (First Online): 25092012
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
An Efficient Implementation Of Floating Point Multiplier
ASIYA THAPASWIN PATTAN
M. Tech Student, Dept of ECE ,
Vaagdevi Institute of Technology & Science, Proddatur, Kadapa (DT), AP516361

d. ASLAM
HOD, Dept. of ECE,
Vaagdevi Institute of Technology & Science, Proddatur, Kadapa (DT), AP516361
AbstractThispaper describes an efficient implementation of an IEEE 754 single precision floating point multiplier targeted for Xilinx Virtex5 FPGA. VHDL is used to implement a technologyindependent pipelined design. The multiplier implementation handles the overflow and underflow cases. Rounding is not implemented to give more precision when using the multiplier in a multiply and Accumulate (MAC) unit. With latency of three clock cycles the design achieves 301 MFLOPs.The multiplier was verified against Xilinx floating point multiplier core.
Keywords floating point; multiplication; FPGA; CAD design flow.

INTRODUCTION

Floating point numbers are one possible way of representing real numbers in binary format; the IEEE 754 standard presents two different floating point formats, Binary interchange format and Decimal interchange format. Multiplying floating point numbers is a critical requirement for DSP applications involving large dynamic range. This paper focuses only on single precision normalized binary interchange format. Fig. 1 shows the IEEE 754 single precision binary format representation; it consists of a one bit sign (S), an eight bit exponent (E), and a twenty three bit fraction (M or Mantissa). An extra bit is added to the fraction to form what is called the significand1. If the exponent is greater than 0 and smaller than 255, and there is 1 in the MSB of the significand then the number is said to be a normalized number; in this case the real number is represented by (1)
V.RAMESH
Assistant Professor, Dept. of ECE,
Vaagdevi Institute of Technology & Science, Kadapa, Proddatur, Kadapa Dt. A.P.516361
Figure1. IEEE single precision floating point format
Z = (1S) * 2(E – Bias)* (1.M)
Where
M = m22 21+ m21 22+ m20 23++ m1 222+ m0 223;
Bias = 127.
Multiplying two numbers in floating point format is done by 1 adding the exponent of the two numbers then subtracting the bias from their result, 2 multiplying the significand of the two numbers, and 3 calculating the sign by XORing the sign of the two numbers. In order to represent the multiplication result as a normalized number there should be 1 in the MSB of the result (leading one). Floatingpoint implementation on FPGAs has been the interest of many researchers.

FLOATING POINT MULTIPLICATION ALGORITHM
As stated in the introduction, normalized floating point numbers have the form of
Z= (1S) * 2 (E – Bias) * (1.M). To multiply two floating point numbers the following is done:

Multiplying the significand; i.e. (1.M1*1.M2)

Placing the decimal point in the result

Adding the exponents; i.e. (E1 + E2 Bias)

Obtaining the sign; i.e. s1 xor s2

Normalizing the result; i.e. obtaining 1 at the MSB of the results significand

Rounding the result to fit in the available bits

Checking for underflow/overflow occurrence
Consider a floating point representation similar to the IEEE 754 single precision floating point format, but with a reduced number of mantissabits (only 4) while
still retaining the hidden 1 bit for normalized numbers:
A = 0 10000100 0100 = 40,
B = 1 10000001 1110 = 7.5
To multiply A and B

Multiply significand: 1.0100
Ã— 1.1110
00000
10100
10100
10100
10100
1001011000

Place the decimal point:
10.01011000

Add exponents: 10000100
+ 10000001
In this paper we present a floating point multiplier in which rounding support isnt implemented. Rounding support can be added as a separate unit that can be accessed by the multiplier or by a floating point adder, thus accommodating for more precision if the multiplier is connected directly to an adder in a MAC unit. Fig.2 shows the multiplier structure; Exponents addition, Significand multiplication, and Results sign calculation areindependent and are done in parallel. The significand multiplication is done on two 24 bit numbers and results in a 48 bit product, which we will call the intermediate product (IP). The IP is represented as (47 downto 0) and the decimal point is located between bits 46 and 45 in the IP. The following sections detail each block of the floating point multiplier.
100000101
The exponent representing the two numbers is already shifted/biased by the bias value (127) and is not the true exponent; i.e. EA
= EAtrue + bias and EB = EBtrue + bias And
EA + EB = EAtrue + EBtrue + 2 bias So we should subtract the bias from
the resultant exponent otherwise the bias will be added twice.
100000101
– 01111111 10000110

Obtain the sign bit and put the result together:
1 10000110 10.01011000

Normalize the result so that there is a 1 just before the radix point (decimal point). Moving the radix point one place to the left increments the exponent by 1; moving one place to the right decrements the
Exponent by 1.
1 10000110 10.01011000 (before
normalizing)
1 10000111 1.001011000
(normalized)
The result is (without the hidden bit):
1 10000111 00101100

The mantissa bits are more than 4 bits (mantissa available bits); rounding is needed. If we applied the

Truncation rounding mode then the stored value is:
1 10000111 0010.
Figure 2. Floating point multiplier block diagram


HARDWARE OF FLOATING POINT MULTIPLIER

Sign bit calculation
Multiplying two numbers results in a negative sign number if one of the multiplied numbers is of a negative value. By the aid of a truth table we find that this can be obtained by XORing the sign of two inputs.

Unsigned Adder (for exponent addition)
This unsigned adder is responsible for adding the exponent of the first input to the exponent of the second input and subtracting the Bias (127) from the addition result (i.e. A_exponent + B_exponent – Bias). The result of this stage is called the intermediate exponent. The add operation is done on 8 bits, and there is no need for a quick result because most of the calculation time is spent in the significand multiplication process (multiplying 24 bits by
24 bits); thus we need a moderate exponent adder and a fast significand multiplier. An 8bit ripple carry adder is used to add the two input exponents. As shown in Fig. 3 a ripple carry adder is a chain of cascaded full adders and one
half adder; each full adder has three inputs (A, B, Ci) and two outputs (S, Co). The carry out (Co) of each adder is fed to the next full adder (i.e. each carry bit "ripples" to the next full adder).
Figure 3.Ripple Carry Adder
The addition process produces an 8 bit sum (S7 to S0) and acarry bit (Co, 7). These bits are concatenated to form a 9 bit addition result (S8 to S0) from which the Bias is subtracted. The Bias is subtracted using an array of ripple borrow subtractors.
A normal subtractor has three inputs (minuend (S), subtrahend (T), Borrow in (Bi)) and two outputs (Difference (R), Borrow out (Bo)).
The subtractor logic can be optimized i one of its inputs is a constant value which is our case, where the Bias is constant (12710 = 0011111112).
Table I shows the truth table for a 1bit subtractor with the input T equal to 1 which we will call one subtractor (OS)
TABLE I. 1BIT SUBTRACTOR WITH THE INUT T=1
The Boolean Equation (2) and (3) represent this subtractor:
Difference(R) = (2)
Borrowout (Bo) = (3)
Figure 4. 1bit subtractor with the input T = 1
Table II shows the truth table for a 1 bit subtractor with the input T equal to 0 which we will call zero subtractor (ZS)
TABLEII.1BIT SUBTRACTOR WITH THE INUT T=0
The Boolean Equation (4) and (5) represent this subtractor:
Difference(R) = (4)
Borrowout (Bo) = (5)
Figure 5. 1bit subtractor with the input T = 0
Fig. 6 shows the Bias Subtractor which is a chain of 7 one subtractors (OS) followed by 2 zero subtractors (ZS); the borrow output of each Subtractor is fed to the next Subtractor. If an underflow occurs then Eresult < 0 and the number is out of the IEEE 754 single precision normalized numbers range; in this case the output is signaled to 0 and an underflow flag is asserted.
Figure6. Ripple Borrow Subtractor

Unsigned Multiplier (for significand multiplication)
This unit is responsible for multiplying the unsigned significand and placing the decimal point in the multiplication product. The result of significand multiplication will be called the intermediate product (IP).
The unsigned significand multiplication is done on 24 bit. Multiplier performance should be taken into consideration so as not to affect the whole multipliers performance. A 24×24 bit carry save multiplier architecture is used as it has a moderate speed with a simple architecture. In the carry save multiplier, the carry bits are passed diagonally downwards (i.e. the carry bit is propagated to the next stage). Partial products are made by ANDing the inputs together and passing them to the appropriate adder.
Carry save multiplier has three main stages: 1 The first stage is an array of half
adders.

The middle stages are arrays of full adders.
The number of middle stages is equal to the significand size minus two.

The last stage is an array of ripple carry adders. This stage is called the vector merging stage.
The number of adders (Half adders and Full adders) in each stage is equal to the significand size minus one. For example, a 4×4 carry save multiplier is shown in Fig. 7 and it has the following stages:

The first stage consists of three half adders.

Two middle stages; each consists of three full adders.

The vector merging stage consists of one half adder and two full adders.
The decimal point is between bits 45 and 46 in the significand multiplier result. The multiplication time taken by the carry save multiplier is determined by its critical path. The critical path starts at the AND gate of the first partial products (i.e. a1b0 and a0b1), passes through the carry logic of the first half adder and the carry logic of the first full adder of the middle stages, then passes through all the vector merging adders. The critical path is marked in bold in Fig. 7
Figure7. 4×4 bit Carry Save multiplier
In Fig. 7:
1 Partial product: aibj = ai and bj 2 HA: half adder
3 FA: full adder



Normalizer
The result of the significand multiplication (intermediate product) must be normalized to have a leading 1 just to the left of the decimal point (i.e. in the bit 46 in the intermediate product). Since the inputs are normalized numbers then the intermediate product has the leading one at bit 46 or 47

If the leading one is at bit 46 (i.e. to the left of the decimal point) then the intermediate product is already a normalized number and no shift is needed.

If the leading one is at bit 47 then the intermediate product is shifted to the right and the exponent isIncremented by 1.
The shift operation is done using combinational shift logic made by multiplexers.
Fig. 8 shows a simplified logic of a Normalizer that has an 8 bit intermediate product input and a 6 bit intermediate exponent input.
Figure8. Simplified Normalizer logic


UNDERFLOW/OVERFLOW DETECTION
Overflow/underflow means that the results exponent is too large/small to be represented in the exponent field. The Exponent of the result must be 8 bits in size, and must be between 1 and 254 otherwise the value is not a normalized one. Between 1 and 254 otherwise the value is not a normalized one.
An overflow may occur while adding the two exponents or during normalization. Overflow due to exponent addition may be compensated during subtraction of the bias; resulting in a normal output value (normal operation). An underflow may occur while subtracting the bias to form the intermediate exponent. If the intermediate exponent < 0 then its an underflow that can never be compensated; if the intermediate exponent = 0 then its an underflow that may be compensated during normalization by adding 1 to it.
Assume that E1 and E2 are the exponents of the two numbers A and B respectively;
The results exponent is calculated by (6) Eresult= E1 + E2 127 (6)
E1 and E2 can have the values from 1 to 254; resulting in Eresult having values from – 125 (2127) to 381 (508127); but for
normalized numbers, Eresult can only have the values from 1 to 254. Table III summarizes the
Eresult different values and the effect of normalization on it.
TABLE III. NORMALIZATION EFFECT ON RESULTS EXPONENT AND OVERFLOW/UNDERFLOW DETECTION

PIPELINING THE MULTIPLIER
In order to enhance the performance of the multiplier, three pipelining stages are used to divide the critical path thus increasing the maximum operating frequency of the multiplier. The pipelining stages are imbedded at the following locations:

In the middle of the significand multiplier, and in the middle of the exponent adder (before the bias subtraction).

after the significand multiplier, and after the exponent adder.

At the floating point multiplier outputs (sign, exponent and mantissa bits).
Fig. 9 shows the pipelining stages as dotted lines.
Figure 9. Floating point multiplier with pipelined stages
Three pipelining stages mean that there is latency in the output by three clocks. The synthesis tool retiming option was used so that the synthesizer uses its optimization logic to better place the pipelining registers across the critical path


RESULT
Fig.10 shows the simulation result of top multiplier.
Figure10. Simulation result of top multiplier
Fig.11 shows the RTL diagram of top multiplier which is having inputs A, B of 32 bit and output of 56bit.
Figure11.RTL diagram of top multiplier

IMPLEMENTATION AND TESTING
The whole multiplier (top unit) was tested against the Xilinx floating point multiplier core generated by Xilinx coregen. Xilinx core was customized to have two flags to indicate overflow and underflow, and to have a maximum latency of three cycles. Xilinx core implements the round to nearest rounding mode.
The area of Xilinx core is less than the implemented floating point multiplier because the latter doesnt truncate/round the 48 bits result of the mantissa multiplier which is reflected in the amount of function generators and registers used to perform operations on the extra bits; also the speed of Xilinx core is
affected by the fact that it implements the round to nearest rounding mode.

CONCLUSIONS AND FUTURE WORK This paper presents an implementation
of a floating point multiplier that supports the
IEEE 7542008 binary interchange format; the multiplier doesnt implement rounding and just presents the significand multiplicaion result as is (48 bits); this gives better precision if the whole 48 bits are utilized in another unit; i.e. a floating point adder to form a MAC unit. The design has three pipelining stages and after implementation on a Xilinx Virtex5 FPGA it achieves 301 MFLOPs.
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N. Shirazi, A. Walters, and P. Athanas, Quantitative Analysis of Floating Point Arithmetic on FPGA Based Custom Computing Machines, Proceedings of the IEEE Symposium on FPGAs for Custom Computing Machines (FCCM95), pp.155162, 1995.

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Mrs.Asiya Thapaswin Pattan, is currently doing post graduation in Vaagdevi institute of Tech & Science, Proddatur, Kadapa (Dt), A.P with the specialization of VLSI
Mr.V.Ramesh,is an Assistant Professor in ECE dept. at Vaagdevi institute of Tech & Science, Proddatur. He obtained his B.Tech degree in ECE from MeRITS,JNTUA, Udayagiri in 2007, M.Tech degree in Electronic Instrumentation and Communication Systems from S.V.University, Tirupati in 2009. He has 4years of teaching experience and his area of interest includes Electronics instrumentation and Antennas. He has published 5 papers in referred international journals and also 3 papers at national level conferences.
Mr.C.Mahammed Aslam is the HOD,Dept.of ECE at Vagdevi institute of Tech & Science, Proddatur.He obtained his B.Tech degree in ECE from Dr.Babasaheb Ambedkar Maratwada University,Aurangabad in 1997,M.Tech degree in Digital Electronics and Computer Science from JNTU,Hyderabad in 2007.He ahs 11 years of teaching experience.He registered his Ph.D from JNTU,Anantapur in digital Image Processing. His area of interest is Microprocessor and EDC circuits. He has published 3 papers in international journals.