Design and Implementation of High Speed Area Efficient Double Precision Floating Point Arithmetic Unit

IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 10, Issue 1, Ver. 1 (Jan - Feb. 2015), PP 49-54
www.iosrjournals.org
DOI: 10.9790/2834-10114954 www.iosrjournals.org 49 | Page
Design and Implementation of High Speed Area Efficient Double
Precision Floating Point Arithmetic Unit
Onkar Singh1
, Kanika Sharma2
1
Arni University, Kathgarh, Indora, HP, India.
2
NITTTR, Chandigarh. India.
Abstract: A floating-point arithmetic unit designed to carry out operations on floating point numbers. Floating
point numbers can support a much wider range of values than fixed point representation. Floating Point units
are mainly used in high speed objects recognition system, high performance computer systems, embedded
systems, mobile applications. Latch based design is implemented in the proposed work so the longer
combinational paths can be compensated by shorter path delays in the subsequent logic gates. That is why the
performance has increased in the design. All four individual units addition, subtraction, multiplication and
division are designed using Verilog verified by using Questa Sim and implemented on vertex-5 FPGA
Keywords: Floating Point, IEEE, FPGA, Vertex-5, Double Precision, Verilog, Arithmetic Unit
I. Introduction
A floating point arithmetic unit designed to carry out operations on floating point numbers. Floating
point arithmetic unit is widely used in high speed objects recognition system, high performance computer
systems, embedded systems, mobile applications and signal processing applications. Floating point
representation can support a much wider range of values than fixed point representation. To represent very large
or small values, wide range is required as the integer representation is no longer appropriate. These values can
be represented using the IEEE-754 standard based floating point representation. The design has been
implemented on latches so the longer combinational paths can be compensated by shorter path delays in the
subsequent logic gates. That is why the performance has increased in the design.
IEEE Single Precision Format: The IEEE single precision format uses 32 bits for representing a floating point
number, divided into three subfields, as illustrated in figure 1. The first field is the sign bit for the fraction part.
The next field consists of 8 bits which are used for exponent the third field consists of the remaining 23 bits and
is used for the fractional part.
Sign Exponent Fraction
1 bit 8 bits 23 bits
Fig 1: IEEE format for single precision
IEEE Double Precision Format: The IEEE double precision format uses 64 bits for representing a floating
point number, as illustrated in figure 2. The first bit is the sign bit for the fraction part. The next 11 bits are used
for the exponent, and the remaining 52 bits are used for the fractional part.
Sign Exponent Fraction
1 bit 11 bits 52 bits
Fig 2: IEEE format for double precision
II. Implementation Of Double Precision Floating Point Arithmetic Unit
The block diagram of the proposed floating point arithmetic unit is given in figure 3. The unit supports
four arithmetic operations: Add, Subtract, Multiply and Divide. All arithmetic operations have been carried out
in four separate modules one for addition, one for subtraction, one for multiplication and one for division as
shown in figure 3. In this unit one can select operation to be performed on the 64-bit operands by a 3-bit op-
code and the same op-code selects the output from that particular module and connects it to the final output of
the unit. Particular exception signal will be high whenever that type of exception will occur.

Design and implementation of high speed area efficient double precision floating point arithmetic unit
Fig 4: RTL view of double precision floating point
arithmetic unit
Fig 3: Block diagram of double precision floating
point arithmetic unit
The floating point arithmetic unit consists of following blocks
2.1) Fpu_Add- Floating Point adder
2.2) Fpu_Sub- Floating Point Subtractor
2.3)Fpu_Mul-Floating Point Multiplier
2.4) Fpu_Div- Floating Point Division
2.5)Fpu_Round-Floating Point Rounding Unit
2.6) Fpu_Exception- Floating Point Exception Unit
2.1) Fpu_Add- Floating Point adder- Two floating point numbers are added as shown below.
(f1 x 2e1
) + (f2 x 2e2
) = F x 2E
In order to add two fractions, the associated exponents must be equal. Thus, if the two exponents are
different, we must un normalize one of the fractions and adjust the exponents accordingly. The smaller number
is the one that should adjusted so that if significant digits are lost, the effect is not significant. In this module two
64-bit numbers are added and after going through rounding and exception part final added result will come to
the output.
2.2) Fpu_Sub- Floating Point Subtractor- Two floating point numbers are subtracted as shown below.
(f1 x 2e1
) - (f2 x 2e2
) = F x 2E
In order to subtract two fractions, the associated exponents must be equal. Thus, if the two exponents
are different, we must un normalize one of the fractions and adjust the exponents accordingly. The smaller
number is the one that should adjusted so that if significant digits are lost, the effect is not significant. In this
module two 64-bit numbers are subtracted and after going through rounding and exception part final subtracted
result will come to the output.
2.3) Fpu_Mul- Floating Point Multiplier-Two floating point numbers are multiplied as shown below.
(f1 x 2e1
) x (f2 * 2e2
) = (f1 x f2) x 2(e1+e2)
= F x 2E
In this module two 64-bit numbers are multiplied using sub multipliers and after going through
rounding and exception part final multiplied result will come to the output. The mantissa of operand A are
stored in the 53-bit register (mul_a). The mantissa of operand B are stored in the 53-bit register (mul_b).
Multiplying all 53 bits of mul_a by 53 bits of mul_b would result in a 106-bit wide product and a 53 by 53 bit
multiplier is not available in the most popular Xilinx FPGAs, so the multiply would be broken down into
smaller multiplies and the results would be added together to give the final 106-bit product. the module
(fpu_mul) breaks up the multiply into smaller 24-bit by 17-bit multiplies. The Xilinx Virtex5 device contains

DSP48E slices with 25 by 18 twos complement multipliers, which can perform a 24 by 17-bit multiply. The
multiply is broken up as follows:
Multiplier_1 = mul_a[23:0] x mul_b[16:0]
Multiplier_4= mul_a[23:0] x mul_b[52:51]
Multiplier_7= mul_a[40:24] x mul_b[52:34]
The multiplier (1-10) are added together, with the appropriate offsets based on which part of the mul_a
and mul_b arrays they are multiplying. The summation of the products is accomplished by adding one product
result to the previous product result instead of adding all 10 multiplier (1-10) together in one summation. The
final 106-bit product is stored in register (product). The exponent fields of operands A and B are added together
and then the value (1022) is subtracted from the sum of A and B. If the resultant exponent is less than 0, than the
(product) register needs to be right shifted by the amount. The final exponent of the output operand will be 0 in
this case, and the result will be a denormalized number.
2.4) Fpu_Div- Floating Point Division - Two floating point numbers are divided as shown below.
(f1 x 2e1
) / (f2 x 2e2
) = (f1 / f2) x 2(e1-e2)
= F x 2E
In this module two 64-bit numbers are divided and after going through rounding and exception part
final divided result will come to the output. The leading „1‟ (if normalized) and mantissa of operand A is the
dividend, and the leading „1‟ (if normalized) and mantissa of operand B is the divisor. In division one bit of the
quotient calculated each clock cycle based on a comparison between the dividend and divisor register. If the
dividend is greater than the divisor, the quotient bit is „1‟, and then the divisor is subtracted from the dividend,
and the resulting difference is shifted one bit to the left, and after shifting it becomes the dividend for the next
clock cycle. And in another case if the dividend is less than the divisor, the dividend is shifted one bit to the left,
and then this shifted value becomes the dividend for the next clock cycle. Repeat the steps by the number of bits
time. The number in the dividend place gives remainder value and quotient place gives quotient value
2.5) Fpu_Round-Floating Point Rounding Unit - Rounding module is used to modifies a number and fit it in
the destination‟s format. The various rounding modes are written below.
2.5.1) Round to nearest even: This is the standard default rounding. The value is rounded down or up to the
nearest infinitely precise result.
2.5.2) Round-to-Zero: Basically in this mode the number will not be rounded. The excess bits will simply get
truncated, e.g. 5.48 will be truncated to 5.5
2.5.3) Round-Up: In this mode the number will be rounded up towards +∞, e.g. 6.4 will be rounded to 7, while -
5.4 to -5
2.5.4) Round-Down: The opposite of round-up, the number will be rounded up towards -∞, e.g. 6.4 will be
rounded to 6, while -5.4 to -6
2.6) Fpu_Exception- Floating Point Exception Unit- Exception occurs when an operation on some particular
operands has no outputs suitable for a reasonable application.
The five possible exceptions are:
2.6.1) Invalid: Operation are like square root of a negative number, returning of NaN by default, etc., output of
which does not exist.
2.6.2) Division by zero: It is an operation on a finite operand which gives an exact infinite result for e.g., 1/0 or
log (0) that returns positive or negative infinity by default.
2.6.3) Overflow: It occurs when an operation results a very large number that can‟t be represented correctly i.e.
which returns ±infinity by default (for round-to-nearest mode).
2.6.4) Underflow: It occurs when an operation results very small i.e. outside the normal range and inexact by
default.
2.6.5) Inexact: It occurs whenever the result of an arithmetic operation is not exact due to the restricted exponent
or precision range.

III. Synthesis, Timing And Simulation Result
3.1) Synthesis Result
DEVICE UTILIZATION SUMMARY
Logic Utilization Used Available Utilization
Number of Slice Registers 4762 28800 16%
Number of Slice LUTs 6525 28800 22%
Number of fully used LUT-FF pairs 3013 7600 36%
Number of bonded IOBs 206 220 93%
Number of BUFG/BUFGCTRLs 6 32 18%
Number of DSP48Es 9 48 18%
3.2) Timing Result
Minimum Period 3.817ns (Maximum Frequency: 262.006MHz)
Minimum Input arrival time before clocks 3.900ns
Maximum output required time after clocks 2.775ns
3.3) Simulation Result- The simulation results of double precision floating point arithmetic unit (Addition,
Subtraction, Multiplication and Division) is shown in fig 5, fig 6, fig 7, fig 8 respectively. It is calculated for the
two input operands of 64 bits each. The reset signal is kept low throughout the simulation, so that operands are
initialised all at once, then at the high of enable signal, operation of the two operands are calculated. After
calculating the result, the result goes into fpu_round and then goes into fpu_exceptions. From fpu_exceptions
the out signal gives the output. In the waveforms clock defines the applied frequency (262.006MHz) to the
signals. Fpu_op defines the operation to be preformed that is 0=addition,1=subtraction, 2=multiplication and
3=division. Opa1 and Opa1 defines the input operand one and input operand two respectively. The r_mode
signal defines the various rounding modes (00=Round to nearest even, 01=Round-to-Zero, 10=Round-Up,
11=Round-Down. Fpu_out defines the final output of the signals.
3.3.1) Simulation Result of floating point addition- It is calculated for the two input operands of 64 bits each.
15 clock cycles are required by floating point unit to complete addition process. As frequency is 262.006MHz so
one clock cycle completes 3.82ns and 15 clock cycles completes in 3.82ns x 15 =57.3ns. Therefore the addition
process completes in 57.3ns.
Fig 5: Simulation Result of Floating Point Addition
Operation Time taken by modules in ns
Addition 57.255ns (15 cycles)
Subtraction 57.255ns (15 cycles)
Multiplication 57.255ns (15 cycles)
Division 259.556ns (68 cycles)

3.3.2) Simulation result of floating point subtraction- It is calculated for the two input operands of 64 bits
each. 15 clock cycles are required by floating point unit to complete subtraction process. As frequency is
262.006MHz so one clock cycle completes 3.82ns and 15 clock cycles completes in 3.82ns x 15 =57.3ns.
Therefore the subtraction process completes in 57.3ns.
Fig 6: Simulation Result of Floating Point Subtraction
3.3.3) Simulation result of floating point multiplication- It is calculated for the two input operands of 64 bits
each. 15 clock cycles are required by floating point unit to complete multiplication process. As frequency is
262.006MHz so one clock cycle completes 3.82ns and 15 clock cycles completes in 3.82ns x 15 =57.3ns.
Therefore the multiplication process completes in 57.3ns.
Fig 7: Simulation Result of Floating Point Multiplication
3.3.4) Simulation result of floating point division- It is calculated for the two input operands of 64 bits each.
68 clock cycles are required by floating point unit to complete division process. As frequency is 262.006MHz so
one clock cycle completes 3.82ns and 68 clock cycles completes in 3.82ns x 68 =259.76ns. Therefore the
division process completes in 259.76ns.

Fig 8: Simulation Result of Floating Point Division
IV. Conclusion
This paper presents a high performance implementation of double precision floating point arithmetic
unit. The complete design is captured in Verilog Hardware description language (HDL), tested in simulation
using Questa Sim, placed and routed on a Vertex 5 FPGA from Xilinx.It works on Maximum Frequency of
262.006MHz. When synthesized, this module used 16% number of slice registers, 22% Number of Slice LUTS,
and 36% number of fully used LUT-FF pairs. The overall performance is increased in this design. The proposed
work can be further proceed by adding some more modules like square root, logarithmic units to the floating
point unit and also the complete design can be implemented on high performance vertex-6 FPGA.
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Design and Implementation of High Speed Area Efficient Double Precision Floating Point Arithmetic Unit

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Design and Implementation of High Speed Area Efficient Double Precision Floating Point Arithmetic Unit