Enabling Approximate Storage through Lossy Media Data Compression
| dc.contributor.author | Worek, Brian David | en |
| dc.contributor.committeechair | Ampadu, Paul K. | en |
| dc.contributor.committeemember | Ha, Dong S. | en |
| dc.contributor.committeemember | Schaumont, Patrick R. | en |
| dc.contributor.committeemember | Athanas, Peter M. | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2019-02-09T09:00:19Z | en |
| dc.date.available | 2019-02-09T09:00:19Z | en |
| dc.date.issued | 2019-02-08 | en |
| dc.description.abstract | Memory capacity, bandwidth, and energy all continue to present hurdles in the quest for efficient, high-speed computing. Recognition, mining, and synthesis (RMS) applications in particular are limited by the efficiency of the memory subsystem due to their large datasets and need to frequently access memory. RMS applications, such as those in machine learning, deliver intelligent analysis and decision making through their ability to learn, identify, and create complex data models. To meet growing demand for RMS application deployment in battery constrained devices, such as mobile and Internet-of-Things, designers will need novel techniques to improve system energy consumption and performance. Fortunately, many RMS applications demonstrate inherent error resilience, a property that allows them to produce acceptable outputs even when data used in computation contain errors. Approximate storage techniques across circuits, architectures, and algorithms exploit this property to improve the energy consumption and performance of the memory subsystem through quality-energy scaling. This thesis reviews state of the art techniques in approximate storage and presents our own contribution that uses lossy compression to reduce the storage cost of media data. | en |
| dc.description.abstractgeneral | Computer memory systems present challenges in the quest for more powerful overall computing systems. Computer applications with the ability to learn from large sets of data in particular are limited because they need to frequently access the memory system. These applications are capable of intelligent analysis and decision making due to their ability to learn, identify, and create complex data models. To meet growing demand for intelligent applications in smartphones and other Internet connected devices, designers will need novel techniques to improve energy consumption and performance. Fortunately, many intelligent applications are naturally resistant to errors, which means they can produce acceptable outputs even when there are errors in inputs or computation. Approximate storage techniques across computer hardware and software exploit this error resistance to improve the energy consumption and performance of computer memory by purposefully reducing data precision. This thesis reviews state of the art techniques in approximate storage and presents our own contribution that uses lossy compression to reduce the storage cost of media data. | en |
| dc.description.degree | MS | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:18607 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/87561 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | approximate storage | en |
| dc.subject | lossy compression | en |
| dc.subject | data-intensive | en |
| dc.title | Enabling Approximate Storage through Lossy Media Data Compression | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | MS | en |
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