Date of Award:
Doctor of Philosophy (PhD)
Electrical and Computer Engineering
Vicki H. Allan
Rapid developments of the AI domain has revolutionized the computing industry by the introduction of state-of-art AI architectures. This growth is also accompanied by a massive increase in the power consumption. Near-Theshold Computing (NTC) has emerged as a viable solution by offering significant savings in power consumption paving the way for an energy efficient design paradigm. However, these benefits are accompanied by a deterioration in performance due to the severe process variation and slower transistor switching at Near-Threshold operation. These problems severely restrict the usage of Near-Threshold operation in commercial applications. In this work, a novel AI architecture, Tensor Processing Unit, operating at NTC is thoroughly investigated to tackle the issues hindering system performance. Research problems are demonstrated in a scientific manner and unique opportunities are explored to propose novel design methodologies.
Gundi, Noel Daniel, "Reclaiming Fault Resilience and Energy Efficiency With Enhanced Performance in Low Power Architectures" (2023). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 8894.
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