The Experts below are selected from a list of 1614 Experts worldwide ranked by ideXlab platform
Wonyong Sung - One of the best experts on this subject based on the ideXlab platform.
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fixed point optimization utility for c and c based digital signal processing programs
IEEE Transactions on Circuits and Systems Ii: Analog and Digital Signal Processing, 1998Co-Authors: Seehyun Kim, Kiil Kum, Wonyong SungAbstract:Fixed-point optimization utility software is developed that can aid scaling and wordlength determination of digital signal processing algorithms written in C or C++. This utility consists of two programs: the range estimator and the fixed-point simulator. The former estimates the ranges of floating-point Variables for purposes of automatic scaling, and the latter translates floating-point programs into fixed-point equivalents to evaluate the fixed-point performance by simulation. By exploiting the operator overloading characteristics of C++, the range estimation and the fixed-point simulation can be conducted by simply modifying the Variable Declaration of the original program. This utility is easily applicable to nearly all types of digital signal processing programs including nonlinear, time-varying, multirate, and multidimensional signal processing algorithms. In addition, this software can be used to compare the fixed-point characteristics of different implementation architectures. An optimization example for an 8/spl times/8 inverse discrete cosine transform (IDCT) architecture that conforms to the IEEE standard specifications is presented. The optimized results require 8% fewer gates when compared with the previous best implementation.
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fixed point optimization utility for c and c based digital signal processing programs
VLSI Signal Processing VIII, 1995Co-Authors: Seehyun Kim, Kiil Kum, Wonyong SungAbstract:Two fixed-point optimization utility programs, the range estimator and the fixed-point simulator, are developed for scaling and wordlength determination of digital signal processing algorithms written in C or C++ language. By exploiting the operator overloading characteristics of C++ language, range estimation and fixed-point simulation can be conducted just by modifying the Variable Declaration of the original floating-point digital signal processing program. Since this utility evaluates the range and the fixed-point performance by simulation, not by analytical methods, it is easily applicable to nearly all type of digital signal processing algorithms including non-linear and time-varying systems. In addition, this utility software can be used for comparing the fixed-point characteristics of different implementation architectures.
Seehyun Kim - One of the best experts on this subject based on the ideXlab platform.
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fixed point optimization utility for c and c based digital signal processing programs
IEEE Transactions on Circuits and Systems Ii: Analog and Digital Signal Processing, 1998Co-Authors: Seehyun Kim, Kiil Kum, Wonyong SungAbstract:Fixed-point optimization utility software is developed that can aid scaling and wordlength determination of digital signal processing algorithms written in C or C++. This utility consists of two programs: the range estimator and the fixed-point simulator. The former estimates the ranges of floating-point Variables for purposes of automatic scaling, and the latter translates floating-point programs into fixed-point equivalents to evaluate the fixed-point performance by simulation. By exploiting the operator overloading characteristics of C++, the range estimation and the fixed-point simulation can be conducted by simply modifying the Variable Declaration of the original program. This utility is easily applicable to nearly all types of digital signal processing programs including nonlinear, time-varying, multirate, and multidimensional signal processing algorithms. In addition, this software can be used to compare the fixed-point characteristics of different implementation architectures. An optimization example for an 8/spl times/8 inverse discrete cosine transform (IDCT) architecture that conforms to the IEEE standard specifications is presented. The optimized results require 8% fewer gates when compared with the previous best implementation.
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fixed point optimization utility for c and c based digital signal processing programs
VLSI Signal Processing VIII, 1995Co-Authors: Seehyun Kim, Kiil Kum, Wonyong SungAbstract:Two fixed-point optimization utility programs, the range estimator and the fixed-point simulator, are developed for scaling and wordlength determination of digital signal processing algorithms written in C or C++ language. By exploiting the operator overloading characteristics of C++ language, range estimation and fixed-point simulation can be conducted just by modifying the Variable Declaration of the original floating-point digital signal processing program. Since this utility evaluates the range and the fixed-point performance by simulation, not by analytical methods, it is easily applicable to nearly all type of digital signal processing algorithms including non-linear and time-varying systems. In addition, this utility software can be used for comparing the fixed-point characteristics of different implementation architectures.
Kiil Kum - One of the best experts on this subject based on the ideXlab platform.
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fixed point optimization utility for c and c based digital signal processing programs
IEEE Transactions on Circuits and Systems Ii: Analog and Digital Signal Processing, 1998Co-Authors: Seehyun Kim, Kiil Kum, Wonyong SungAbstract:Fixed-point optimization utility software is developed that can aid scaling and wordlength determination of digital signal processing algorithms written in C or C++. This utility consists of two programs: the range estimator and the fixed-point simulator. The former estimates the ranges of floating-point Variables for purposes of automatic scaling, and the latter translates floating-point programs into fixed-point equivalents to evaluate the fixed-point performance by simulation. By exploiting the operator overloading characteristics of C++, the range estimation and the fixed-point simulation can be conducted by simply modifying the Variable Declaration of the original program. This utility is easily applicable to nearly all types of digital signal processing programs including nonlinear, time-varying, multirate, and multidimensional signal processing algorithms. In addition, this software can be used to compare the fixed-point characteristics of different implementation architectures. An optimization example for an 8/spl times/8 inverse discrete cosine transform (IDCT) architecture that conforms to the IEEE standard specifications is presented. The optimized results require 8% fewer gates when compared with the previous best implementation.
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fixed point optimization utility for c and c based digital signal processing programs
VLSI Signal Processing VIII, 1995Co-Authors: Seehyun Kim, Kiil Kum, Wonyong SungAbstract:Two fixed-point optimization utility programs, the range estimator and the fixed-point simulator, are developed for scaling and wordlength determination of digital signal processing algorithms written in C or C++ language. By exploiting the operator overloading characteristics of C++ language, range estimation and fixed-point simulation can be conducted just by modifying the Variable Declaration of the original floating-point digital signal processing program. Since this utility evaluates the range and the fixed-point performance by simulation, not by analytical methods, it is easily applicable to nearly all type of digital signal processing algorithms including non-linear and time-varying systems. In addition, this utility software can be used for comparing the fixed-point characteristics of different implementation architectures.
Yi Dai - One of the best experts on this subject based on the ideXlab platform.
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Capture-Avoiding and Hygienic Program Transformations (incl. Proofs)
2014Co-Authors: Erdweg Sebastian, Van Der Storm Tijs, Yi DaiAbstract:Program transformations in terms of abstract syntax trees compromise referential integrity by introducing Variable capture. Variable capture occurs when in the generated program a Variable Declaration accidentally shadows the intended target of a Variable reference. Existing transformation systems either do not guarantee the avoidance of Variable capture or impair the implementation of transformations. We present an algorithm called name-fix that automatically eliminates Variable capture from a generated program by systematically renaming Variables. name-fix is guided by a graph representation of the binding structure of a program, and requires name-resolution algorithms for the source language and the target language of a transformation. name-fix is generic and works for arbitrary transformations in any transformation system that supports origin tracking for names. We verify the correctness of name-fix and identify an interesting class of transformations for which name-fix provides hygiene. We demonstrate the applicability of name-fix for implementing capture-avoiding substitution, inlining, lambda lifting, and compilers for two domain-specific languages.Comment: In Proceedings of European Conference on Object-Oriented Programming (ECOOP) 201
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Capture-avoiding and hygienic program transformations
Springer, 2014Co-Authors: Sebastian Erdweg, Tijs Van Der Storm, Yi DaiAbstract:Abstract. Program transformations in terms of abstract syntax trees compromise referential integrity by introducing Variable capture. Variable capture occurs when in the generated program a Variable Declaration accidentally shadows the intended target of a Variable reference. Existing transformation systems either do not guarantee the avoidance of Variable capture or impair the implementation of transformations. We present an algorithm called name-fix that automatically eliminates Variable capture from a generated program by systematically renaming Variables. name-fix is guided by a graph representation of the binding structure of a program, and requires name-resolution algorithms for the source language and the target language of a transformation. name-fix is generic and works for arbitrary transformations in any transformation system that supports origin tracking for names. We verify the correctness of name-fix and identify an interesting class of transformations for which name-fix provides hygiene. We demonstrate the applicability of name-fix for implementing capture-avoiding substitution, inlining, lambda lifting, and compilers for two domain-specific languages.
Daniel J Velleman - One of the best experts on this subject based on the ideXlab platform.
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Variable Declarations in natural deduction
Annals of Pure and Applied Logic, 2006Co-Authors: Daniel J VellemanAbstract:Abstract We propose the use of Variable Declarations in natural deduction. A Variable Declaration is a line in a derivation that introduces a new Variable into the derivation. Semantically, it can be regarded as declaring that the Variable denotes an element of the universe of discourse. Undeclared Variables, in contrast, do not denote anything, and may not occur free in any formula in the derivation. Although most natural deduction systems in use today do not have Variable Declarations, the idea can be traced back to one of the first papers on natural deduction. We show how the use of Variable Declarations in natural deduction leads to a formal system that has a number of desirable features: It is simple, easy to use and understand, and corresponds closely to ordinary informal reasoning. Soundness and completeness of the system are easily proven. Furthermore, the system clarifies the role of the existential instantiation rule in natural deduction.
