Keywords and Phrases
Approximate Computing; Approximate Stochastic Computing; Bit Truncation; Computation; Information Processing; Stochastic Computing
"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.
Stanley, R. Joe
Kosbar, Kurt Louis
Electrical and Computer Engineering
M.S. in Electrical Engineering
National Science Foundation (U.S.)
Missouri University of Science and Technology
x, 53 pages
© 2018 Keerthana Pamidimukkala, All rights reserved.
Thesis - Open Access
Electronic OCLC #
Pamidimukkala, Keerthana, "Generalized adaptive variable bit truncation model for approximate stochastic computing" (2018). Masters Theses. 7777.