Single Precision Floating Point Multiplier

B. Vinoth Kumar

ISBN 10: 3960671555 / ISBN 13: 9783960671558

Published by Anchor Academic Publishing Jul 2017, 2017

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -The Floating Point Multiplier is a wide variety for increasing accuracy, high speed and high performance in reducing delay, area and power consumption. The floating point is used for algorithms of Digital Signal Processing and Graphics. Many floating point multipliers are used to reduce the area that perform in both the single precision and the double precision in multiplication, addition and subtraction.Here, the scientific notations sign bit, mantissa and exponent are used. The real numbers are divided into two components: fixed component of significant range (lack of dynamic range) and exponential component in floating point (largest dynamic range). The authors convert decimal to floating point and normalize the exponent part and rounding operation to reduce latency. The mantissa of two values are multiplied and the exponent part is added. The sign results with exclusive-or are obtained. Then, the final result of shift and add floating point multiplier is compared with booth multiplication.Diplomica Verlag, Hermannstal 119k, 22119 Hamburg 68 pp. Englisch.

Seller Inventory # 9783960671558

Bibliographic Details

• Title: Single Precision Floating Point Multiplier
• Publisher: Anchor Academic Publishing Jul 2017
• Publication Date: 2017
• Language: English
• ISBN 10: 3960671555
• ISBN 13: 9783960671558
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 155H x 5D millimeters 220 millimeters width by 155 millimeters height by 5 millimeters depth
• Weight: 123 grams

About this title

Synopsis

The Floating Point Multiplier is a wide variety for increasing accuracy, high speed and high performance in reducing delay, area and power consumption. The floating point is used for algorithms of Digital Signal Processing and Graphics. Many floating point multipliers are used to reduce the area that perform in both the single precision and the double precision in multiplication, addition and subtraction. Here, the scientific notations sign bit, mantissa and exponent are used. The real numbers are divided into two components: fixed component of significant range (lack of dynamic range) and exponential component in floating point (largest dynamic range). The authors convert decimal to floating point and normalize the exponent part and rounding operation to reduce latency. The mantissa of two values are multiplied and the exponent part is added. The sign results with exclusive-or are obtained. Then, the final result of shift and add floating point multiplier is compared with booth multiplication.