Dissertation/Thesis Abstract

Algorithms and hardware designs for decimal multiplication
by Erle, Mark A., Ph.D., Lehigh University, 2009, 229; 3344791
Abstract (Summary)

Although a preponderance of business data is in decimal form, virtually all floating-point arithmetic units on today's general-purpose microprocessors are based on the binary number system. Higher performance, less circuitry, and better overall error characteristics are the main reasons why binary floating-point hardware (BFP) is chosen over decimal floating-point (DFP) hardware. However, the binary number system cannot precisely represent many common decimal values. Further, although BFP arithmetic is well-suited for the scientific community, it is quite different from manual calculation norms and does not meet many legal requirements.

Due to the shortcomings of BFP arithmetic, many applications involving fractional decimal data are forced to perform their arithmetic either entirely in software or with a combination of software and decimal fixed-point hardware. Providing DFP hardware has the potential to dramatically improve the performance of such applications. Only recently has a large microprocessor manufacturer begun providing systems with DFP hardware. With available die area continually increasing, dedicated DFP hardware implementations are likely to be offered by other microprocessor manufacturers.

This dissertation discusses the motivation for decimal computer arithmetic, a brief history of this arithmetic, and relevant software and processor support for a variety of decimal arithmetic functions. As the context of the research is the IEEE Standard for Floating-point Arithmetic (IEEE 754-2008) and two-state transistor technology, descriptions of the standard and various decimal digit encodings are described.

The research presented investigates algorithms and hardware support for decimal multiplication, with particular emphasis on DFP multiplication. Both iterative and parallel implementations are presented and discussed. Novel ideas are advanced such as the use of decimal counters and compressors and the support of IEEE 754-2008 floating-point, including early estimation of the shift amount, in-line exception handling, on-the-fly sticky bit generation, and efficient decimal rounding. The iterative and parallel, decimal multiplier designs are compared and contrasted in terms of their latency, throughput, area, delay, and usage.

The culmination of this research is the design and comparison of an iterative DFP multiplier with a parallel DFP multiplier. The iterative DFP multiplier is significantly smaller and may achieve a higher practical frequency of operation than the parallel DFP multiplier. Thus, in situations where the area available for DFP is an important design constraint, the iterative DFP multiplier may be an attractive implementation. However, the parallel DFP multiplier has less latency for a single multiply operation and is able to produce a new result every cycle. As for power considerations, the fewer overall devices in the iterative multiplier, and more importantly the fewer storage elements, should result in less leakage. This benefit is mitigated by its higher latency and lower throughput.

The proposed implementations are suitable for general-purpose, server, and main-frame microprocessor designs. Depending on the demand for DFP in human-centric applications, this research may be employed in the application-specific integrated circuits (ASICs) market.

Indexing (document details)
Advisor: Schulte, Michael J., Arnold, Mark G.
Commitee: Davison, Brian D., Pottenger, William M., Schwarz, Eric M., Wagh, Meghanad D.
School: Lehigh University
Department: Computer Engineering
School Location: United States -- Pennsylvania
Source: DAI-B 70/02, Dissertation Abstracts International
Subjects: Electrical engineering, Computer science
Keywords: Binary coded decimals, Decimal multiplication, Fixed-point arithmetic, Floating-point arithmetic, Pipelined multiplications, Serial multiplications
Publication Number: 3344791
ISBN: 9781109042283
Copyright © 2019 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy