Titlebook: Application Specific Processors; Earl E. Swartzlander Book 1997 Kluwer Academic Publishers 1997 ASIC.CMOS.Computer.algorithms.development.

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Modeling the Power Consumption of CMOS Arithmetic Elements,th of the design iterations. Simulation and direct measurement of the performance of test chips is used to evaluate their characteristics, and the results are used to rank the circuits on dynamic power dissipation.

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Variable-Precision, Interval Arithmetic Processors,fy the precision of the computation, determine the accuracy of the results, and recompute inaccurate results with higher precision. The processors support a wide variety of arithmetic operations on variable-precision floating point numbers and intervals. Efficient hardware algorithms and specially d

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Modeling the Power Consumption of CMOS Arithmetic Elements,estimates of the power dissipation of CMOS adders and multipliers, this research aids in the initial circuit choice, thus reducing the number and length of the design iterations. Simulation and direct measurement of the performance of test chips is used to evaluate their characteristics, and the res

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Low Power Digital Multipliers,o reduce switching activity and improve the performance at the algorithm and circuit level are presented. A new concept to reduce switching activity using combinational self-timed elements and bypassing logic blocks to eliminate redundant operations is proposed.

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A Unified View of CORDIC Processor Design,e of partial vector rotations to approximate the expected one. Under different operating modes, this algorithm can be used either to do Givens transformation for vector rotation and vectoring or to evaluate more than a dozen of elementary, trigonometric, and hyperbolic functions. CORDIC processors a

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Multidimensional Systolic Arrays for Computing Discrete Fourier Transforms and Discrete Cosine Transform (DCT) in a multidimensional systolic array. There are extensive applications of fast Fourier transform (FFT) and fast cosine transform (FDCT) algorithms. From the basic principle of fast transform algorithms (breaking the computation in successively smaller computations), we find that the mult

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