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Stagioni: Temperature management to enable near-sensor processing for performance, fidelity, and energy-efficiency of vision and imaging workloads

Abstract Vision processing on traditional architectures is inefficient due to energy-expensive off-chip data movements. Many researchers advocate pushing processing close to the sensor to substantially reduce data movements. However, continuous near-sensor processing raises the sensor temperature, impairing the fidelity of imaging/vision tasks.

The work characterizes the thermal implications of using 3D stacked image sensors with near-sensor vision processing units. The characterization reveals that near-sensor processing reduces system power but degrades image quality. For reasonable image fidelity, the sensor temperature needs to stay below a threshold, situationally determined by application needs. Fortunately, the characterization also identifi... (more)
Created Date 2019
Contributor Kodukula, Venkatesh (Author) / LiKamWa, Robert (Advisor) / Chakrabarti, Chaitali (Committee member) / Brunhaver, John (Committee member) / Arizona State University (Publisher)
Subject Computer engineering / Computer science / Electrical engineering / continuous mobile vision / near-sensor processing / runtime / thermal noise
Type Masters Thesis
Extent 52 pages
Language English
Note Masters Thesis Computer Engineering 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis