Masters Theses

Keywords and Phrases

Approximate Computing; Approximate Stochastic Computing; Bit Truncation; Computation; Information Processing; Stochastic Computing

Abstract

"Stochastic computing as a computing paradigm is currently undergoing revival as the advancements in technology make it applicable especially in the wake of the need for higher computing power for emerging applications. Recent research in stochastic computing exploits the benefits of approximate computing, called Approximate Stochastic Computing (ASC), which further reduces the operational overhead in implementing stochastic circuits. A mathematical model is proposed to analyze the efficiency and error involved in ASC. Using this mathematical model, a new generalized adaptive method improving on ASC is proposed in the current thesis. The proposed method has been discussed with two possible implementation variants - Area efficient and Time efficient. The proposed method has also been implemented in Matlab to compare against ASC and is shown to perform better than previous approaches for error-tolerant applications"--Abstract, page iii.

Advisor(s)

Choi, Minsu

Committee Member(s)

Stanley, R. Joe
Kosbar, Kurt Louis

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Electrical Engineering

Sponsor(s)

National Science Foundation (U.S.)

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2018

Pagination

x, 53 pages

Note about bibliography

Includes bibliographic references (pages 51-52).

Rights

© 2018 Keerthana Pamidimukkala, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 11309

Electronic OCLC #

1041858821

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