Date of Award
2020
Publisher
North Dakota State University
Document Type
Dissertation
Degree Awarded
Doctor of Philosophy (PhD)
Program
Electrical and Computer Engineering
Department
Electrical and Computer Engineering
College
Engineering
Faculty Advisor
Braaten, Benjamin
Description
Static Random Access Memory (SRAM) is a critical component in mobile video processing systems. Because of the large video data size, the memory is frequently accessed, which dominates the power consumption and limits battery life. In energy-efficient SRAM design, a substantial amount of research is presented to discuss the mechanisms of approximate storage, but the content and environment adaptations were never a part of the consideration in memory design. This dissertation focuses on optimization methods for the SRAM system, specifically addressing three areas of Intelligent Energy-Efficient Storage system design. First, the SRAM stability is discussed. The relationships among supply voltage, SRAM transistor sizes, and SRAM failure rate are derived in this section. The result of this study is applied to all of the later work. Second, intelligent voltage scaling techniques are detailed. This method utilizes the conventional voltage scaling technique by integrating self-correction and sizing techniques. Third, intelligent bit-truncation techniques are developed. Viewing environment and video content characteristics are considered in the memory design. The performance of all designed SRAMs are compared to published literature and are proven to have improvement.
Recommended Citation
Gong, Yifu, "Intelligent Energy-Efficient Storage System for Big-Data Applications" (2020). Electrical and Computer Engineering. 149.
https://digitalcommons.ndsu.edu/elec-comp-eng/149
Rights
NDSU policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Handle Identifier
https://hdl.handle.net/10365/31752