Masters Theses


Ramu Seva


"This thesis is comprised of two papers, where the first paper presents a novel approach for parallel implementation of SC using FPGA (Field Programmable Gate Array). This paper makes use of the distributed memory elements of FPGAs (i.e., look-up-tables -LUTs) to achieve this. An attempt has been made to build the stochastic number generators (SNGs) by using the proposed LUT approach. The construction of these SNGs has been influenced by the Quasi-random number sequences, which provide the advantage of reducing the random fluctuations present in the pseudo-random number generators such as LFSR (Linear Feedback Shift Register) as well as the execution time by faster convergence. The results prove that the throughput of the system increases and the execution time is reduced by adopting the proposed technique.

The second paper of the thesis proposes a novel technique referred to as the approximate stochastic computing (ASC) approach focusing on image processing applications to reduce the lengthy computation time of SC with a trade-off in accuracy. The proposed technique is to truncate low-order bits of the image pixel values for SC for faster operation, which also causes an error in the binary to stochastic converted value. Attempts have been made to reduce this error by linearly increasing the clock cycles rather than exponentially. Experimental results from the well-known SC edge detection circuit indicate that the proposed technique is a promising approach for efficient approximate stochastic image processing"--Abstract, page iv.


Choi, Minsu

Committee Member(s)

Beetner, Daryl G.
Cutitaru, Mihail T.


Electrical and Computer Engineering

Degree Name

M.S. in Computer Engineering


Missouri University of Science and Technology

Publication Date

Spring 2017

Journal article titles appearing in thesis/dissertation

  • FPQSC: FPGA based parallel quasi stochastic computing
  • Approximate stochastic computing (ASC) for image processing applications


ix, 43 pages

Note about bibliography

Includes bibliographical references (pages 40-42).


© 2017 Ramu Seva

Document Type

Thesis - Open Access

File Type




Thesis Number

T 11117

Electronic OCLC #