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HDL IMPLEMENTATION AND ANALYSIS OF A RESIDUAL REGISTER FOR A FLOATING-POINT ARITHMETIC UNIT

Akil Kaveti

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Abstract

Processors used in lower-end scientific applications like graphic cards and video game consoles have IEEE single precision floating-point hardware [23]. Double precision offers higher precision at higher implementation cost and lower performance. The need for high precision computations in these applications is not enough to justify the use double precision hardware and the extra hardware complexity needed [23]. Native-pair arithmetic offers an interesting and feasible solution to this problem. This technique invented by T. J. Dekker uses single-length floating-point numbers to represent higher precision floating-point numbers [3]. Native-pair arithmetic has been proposed by Dr. William R. Dieter and Dr. Henry G. Dietz to achieve better accuracy using standard IEEE single precision floating point hardware [1]. Native-pair arithmetic results in better accuracy however it decreases the performance by 11x and 17x for addition and multiplication respectively [2]. The proposed implementation uses a residual register to store the error residual term [2]. This addition is not only cost efficient but also results in acceptable accuracy with 10 times the performance of 64-bit hardware. This thesis demonstrates the implementation of a 32-bit floating-point unit with residual register and estimates the hardware cost and performance.

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Processors used in lower-end scientific applications like graphic cards and video game consoles have IEEE single precision floating-point hardware [23]. Double precision offers higher precision at higher implementation cost and lower performance. The need for high precision computations in these applications is not enough to justify the use double precision hardware and the extra hardware complexity needed [23]. Native-pair arithmetic offers an interesting and feasible solution to this problem. This technique invented by T. J. Dekker uses single-length floating-point numbers to represent higher precision floating-point numbers [3]. Native-pair arithmetic has been proposed by Dr. William R. Dieter and Dr. Henry G. Dietz to achieve better accuracy using standard IEEE single precision floating point hardware [1]. Native-pair arithmetic results in better accuracy however it decreases the performance by 11x and 17x for addition and multiplication respectively [2]. The proposed implementation uses a residual register to store the error residual term [2]. This addition is not only cost efficient but also results in acceptable accuracy with 10 times the performance of 64-bit hardware. This thesis demonstrates the implementation of a 32-bit floating-point unit with residual register and estimates the hardware cost and performance.

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Available abstract

Processors used in lower-end scientific applications like graphic cards and video game consoles have IEEE single precision floating-point hardware [23]. Double precision offers higher precision at higher implementation cost and lower performance. The need for high precision computations in these applications is not enough to justify the use double precision hardware and the extra hardware complexity needed [23]. Native-pair arithmetic offers an interesting and feasible solution to this problem. This technique invented by T. J. Dekker uses single-length floating-point numbers to represent higher precision floating-point numbers [3]. Native-pair arithmetic has been proposed by Dr. William R. Dieter and Dr. Henry G. Dietz to achieve better accuracy using standard IEEE single precision floating point hardware [1]. Native-pair arithmetic results in better accuracy however it decreases the performance by 11x and 17x for addition and multiplication respectively [2]. The proposed implementation uses a residual register to store the error residual term [2]. This addition is not only cost efficient but also results in acceptable accuracy with 10 times the performance of 64-bit hardware. This thesis demonstrates the implementation of a 32-bit floating-point unit with residual register and estimates the hardware cost and performance.

Key concepts: Double-precision floating-point format, Floating-point unit, Single-precision floating-point format, Floating point, Arithmetic, IEEE floating point, Computer science, Residual

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