• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.11
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• pp.47-55
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• 1997

In general, processing flow of the conventional floating-point multiplication consists of either multiplication, addition, normalization, and rounding stage of the conventional floating-point multiplier requries a high speed adder for increment, increasing the overall execution time and occuping a large amount of chip area. A floating-point multiplier performing addition and IEEE rounding in parallel is designed by using the carry select addder used in the addition stage and optimizing the operational flow based on the charcteristics of floating point multiplication operation. A hardware model for the floating point multiplier is proposed and its operational model is algebraically analyzed in this paper. The proposed floating point multiplier does not require and additional execution time nor any high spped adder for rounding operation. Thus, performance improvement and cost-effective design can be achieved by this suggested approach.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.1
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• pp.1-8
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• 2009

This paper proposes a 24-bit floating point multiply and accumulate(MAC) unit that can be used in geometry transformation process in 3D graphics. The MAC unit is composed of floating point multiplier and floating point accumulator. When separate multiplier and accumulator are used, matrix calculation, used in the transformation process, can't use continuous accumulation values. In the proposed MAC unit the accumulator can get continuous input from the multiplier and the calculation time is reduced. The MAC unit uses about 4,300 gates and can be operated at 150 MHz frequency.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.778-780
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• 1999

Arithmetic unit speed depends strongly on the algorithms employed to realize the basic arithmetic operations.(add, subtract multiply, and divide) and on the logic design. Recent advances in VLSI have increased the feasibility of hardware implementation of floating point arithmetic units and microprocessors require a powerful floating-point processing unit as a standard option. This paper describes the design of floating-point multiplier for IEEE 754-1985 Single-Precision operation. Booth encoding algorithm method to reduce partial products and a Wallace tree of 4-2 CSA is adopted in fraction multiplication part to generate the $32{\times}32$ single-precision product. New scheme of rounding and sticky-bit generation is adopted to reduce area and timing. Also there is a true sign generator in this design. This multiplier have been implemented in a ALTERA FLEX EPF10K70RC240-4.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1129-1132
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• 1998

This paper presents a design of special hardware developed for enhancing the floating-point power operations which are actively used at the lighting stage to calculate the specular term in 3D graphics geometry engines. The power operation takes just 4 cycles in our floating-point multiplier while it takes about 100-200 cycles in conventional floating-point units. Although an approximation algorithm is employed in the power operation to reduce the hardware complexity required, the error of power value from the developed floatingpoint multiplier is so minimal that no difference can be found by human eyes.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.47-50
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• 2001

Nowadays ARM7 core is used in many fields such as PDA systems because of the low power and low cost. It is a general-purpose processor, designed for both efficient digital signal processing and controller operations. But the advent of the wireless communication creates a need for high computational performance for signal processing. And then This paper has been designed a floating-point multiplier compatible to IEEE-754 single precision format for ARMTTDMI performance improvement.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.3093-3099
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• 2014

Modern graphic processors, multimedia processors and audio processors mostly use floating-point number. Meanwhile, high-level language such as C and Java uses both single-precision and double precision floating-point number. In this paper, an algorithm which computes the reciprocal of double precision floating-point number using a 32 bit multiplier is proposed. It divides the mantissa of double precision floating-point number to upper part and lower part, and calculates the reciprocal of the upper part with Goldschmidt's algorithm, and computes the reciprocal of double precision floating-point number with calculated upper part reciprocal as the initial value is proposed. Since the number of multiplications performed by the proposed algorithm is dependent on the mantissa of floating-point number, the average number of multiplications per an operation is derived from some reciprocal tables with varying sizes.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.6
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• pp.31-37
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• 2013

Modern graphic processors, multimedia processors and audio processors mostly use floating-point number. High-level language such as C and Java use both single precision and double precision floating-point number. In this paper, an algorithm which computes the reciprocal of double precision floating-point number using a 32 bit multiplier is proposed. It divides the mantissa of double precision floating-point number to upper part and lower part, and calculates the reciprocal of the upper part with Newton-Raphson algorithm. And it computes the reciprocal of double precision floating-point number with calculated upper part reciprocal as the initial value. Since the number of multiplications performed by the proposed algorithm is dependent on the mantissa of floating-point number, the average number of multiplications per an operation is derived from some reciprocal tables with varying sizes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.5
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• pp.1332-1344
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• 1996

This paper presents a pipelined floating point multiplier(FMUL) for superscalar microprocessors that conbines radix-16 recoding scheme based on signed-digit(SD) number system and new rouding and normalization scheme. The new rounding and normalization scheme enable the FMUL to compute sticky bit in parallel with multiple operation and elminate timing delay due to post-normalization. By expoliting SD radix-16 recoding scheme, we can achieves further reduction of silicon area and computation time. The FMUL can execute signle-precision or double-precision floating-point multiply operation through three-stage pipelined datapath and support IEEE standard 754. The algorithm andstructure of the designed multiplier have been successfully verified through Verilog HOL modeling and simulation.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.409-416
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• 2024

With the expansion of floating-point operation requirements for fast high-speed data signal processing and logic operations, the speed of the floating-point operation unit is the key to affect system operation. This paper studies the performance characteristics of different floating-point multiplier schemes, completes partial product compression in the form of carry and sum, and then uses a carry look-ahead adder to obtain the result. Intel Quartus II CAD tool is used for describing Verilog HDL and evaluating performance results of the floating point multipliers. Floating point multipliers are analyzed and compared based on area, speed, and power consumption. The FMAX of modified Booth encoding with Wallace tree is 33.96 Mhz, which is 2.04 times faster than the booth encoding, 1.62 times faster than the modified booth encoding, 1.04 times faster than the booth encoding with wallace tree. Furthermore, compared to modified booth encoding, the area of modified booth encoding with wallace tree is reduced by 24.88%, and power consumption of that is reduced by 2.5%.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1025-1028
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• 1998

Hardware to implement the parallelized Floating-point rounding algorithm is described. For parallelized additions, we propose an addition module which has carry selection logic to generate two results accoring to the input valuse. A multiplication module for parallelized multiplications is also proposed to generate Sum and Carry bits as intermediate results. Since these modules process data in IEEE standard Floatingpoint double precision format, they are designed for 53-bit significands including hidden bits. Multiplication module is designed with a Booth multiplier and an array multiplier.