| dc.contributor.author | Edstrom, Jonathon | |
| dc.description.abstract | Energy efficient memory designs are becoming increasingly important, especially for applications related to mobile video technology and machine learning. The growing popularity of smart phones, tablets and other mobile devices has created an exponential demand for video applications in today’s society. When mobile devices display video, the embedded video memory within the device consumes a large amount of the total system power. This issue has created the need to introduce power-quality tradeoff techniques for enabling good quality video output, while simultaneously enabling power consumption reduction. Similarly, power efficiency issues have arisen within the area of machine learning, especially with applications requiring large and fast computation, such as neural networks. Using the accumulated data knowledge from various machine learning applications, there is now the potential to create more intelligent memory with the capability for optimized trade-off between energy efficiency, area overhead, and classification accuracy on the learning systems. In this dissertation, a review of recently completed works involving video and machine learning memories will be covered. Based on the collected results from a variety of different methods, including: subjective trials, discovered data-mining patterns, software simulations, and hardware power and performance tests, the presented memories provide novel ways to significantly enhance power efficiency for future memory devices. An overview of related works, especially the relevant state-of-the-art research, will be referenced for comparison in order to produce memory design methodologies that exhibit optimal quality, low implementation overhead, and maximum power efficiency. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU policy 190.6.2 | en_US |
| dc.title | Embracing Visual Experience and Data Knowledge: Efficient Embedded Memory Design for Big Videos and Deep Learning | en_US |
| dc.type | Dissertation | en_US |
| dc.type | Video | en_US |
| dc.date.accessioned | 2020-09-29T20:09:32Z | |
| dc.date.available | 2020-09-29T20:09:32Z | |
| dc.date.issued | 2019 | |
| dc.identifier.uri | https://hdl.handle.net/10365/31558 | |
| dc.subject | data mining | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | low-power | en_US |
| dc.subject | memory | en_US |
| dc.subject | SRAM | en_US |
| dc.subject | video | en_US |
| dc.identifier.orcid | 0000-0002-4924-1310 | |
| dc.description.sponsorship | National Science Foundation | en_US |
| dc.description.sponsorship | ND EPSCoR | en_US |
| dc.description.sponsorship | Center for Computationally Assisted Science and Technology (CCAST) | en_US |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | en_US |
| ndsu.degree | Doctor of Philosophy (PhD) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Electrical and Computer Engineering | en_US |
| ndsu.program | Electrical and Computer Engineering | en_US |
| ndsu.advisor | Smith, Scott | |
| ndsu.advisor | Gong, Na | |
